IMS AccessPoint 6 - May 2013

AccessPointNews, views and insights from leading experts in RWE and HEOR

IMS HEALTH ECONOMICS AND OUTCOMES RESEARCHOUTCOMES - Issue 1 Page 1

Massoud Toussiexplains howcreative codingdoubles availablepatient-level data Page 30

Stefan Plantörreveals the criticalpatient endpointsfor AMNOGPage 50

Oncology calls forconvincing evidenceof comparativeeffectiveness saysKarin BergerPage 24

Where nextfor RWE?

The path to value-based healthcarein Latin America

Focus is key tounlocking the valueof real-world,evidence-based data

RWE market impact on medicines: A lens for pharma

VOLUME 3, ISSUE 6MAY 2013

IMS REAL-WORLD EVIDENCE SOLUTIONS ANDHEALTH ECONOMICS & OUTCOMES RESEARCH

Demystifyingpropensity scoring

IMS REAL-WORLD EVIDENCE SOLUTIONS & HEOR

AccessPointNews, views and insights from leading experts in RWE and HEOR

All change for medical devices in GermanyNew regulations bring new potential

but what do early insights show?

page 56

Study designs in observational researchIncreased scrutiny demands rigorous methodology.

page 40

New innovations in outcomes researchHow information technology is transforming the future

of real-world data.

page 18

An RWE lens for pharmaUnique IMS research brings actionable insights into RWE use

and market impact around the world.

page 12

VOLUME 3, ISSUE 6MAY 2013

ACCESSPOINT • VOLUME 3 ISSUE 6 PAGE 1

Contents

NEWS2 IMS SUPPORTS HTA IN ASIA PACIFIC4 DATA LINKAGE ACCELERATES RESEARCH6 IMS INSTITUTE ANALYZES US CARE TRENDS7 PARTNERSHIP COMPLETES PATIENT PATH

INSIGHTS8 RWE PERSPECTIVE

Why focus is key12 RWE MARKET IMPACT

International lens for pharma18 IMS SYMPOSIUM

New age for outcomes research24 ONCOLOGY REAL-WORLD DATA

Identifying valid sources in Europe30 NATURAL LANGUAGE PROCESSING

Optimizing EMR value36 PROPENSITY SCORING Enabling trust in RWE40 ObSERVATIONAL STUDY DESIGNS

Ensuring credible research44 HTA IN LATIN AMERICA Fulfilling evidence demands50 AMNOG ENDPOINTS

Proving patient relevance56 MEDICAL DEVICES IN GERMANY

Understanding new potential

PROJECT FOCUS60 CHRONIC HEPATITIS b

Demonstrating cost-effectiveness in Italy63 RHEUMATOID ARTHRITIS

Treatment pathway decision support

IMS RWES & HEOR OVERVIEW66 ENAbLING YOUR REAL-WORLD SUCCESS

Solutions, locations and expertise

WelcomeWelcome to the latest issue of AccessPoint – a chance to take stock of theevolving landscape and set a course to meet new needs in a market that ismore than ever reliant on our community for success.

In 2013, a noteworthy new direction is emerging as we observe severalpharma companies separating HEOR into two complementary functionalgroups. While structures differ, there are two general themes: HTA excellence – through accelerated scale and focus on HTA support,including modeling and submissions; and real-world evidence (RWE)excellence – by transforming evidence development into a coordinated,at-scale and highly-skilled lifecycle process. This is just the latest in a two-decade search to balance the specialist functional, geographic andtechnical needs for generating and communicating customer-centricevidence. These activities clearly remain interdependent but the signssuggest an enduring organizational trend.

We are also seeing the real market impact of RWE and its influence ondecisions about the use of medicines (page 12), as well as the challengesof implementing systems for HTA (page 44) and addressing the growingdemands for RWE (page 24). At the same time, tremendous strides arebeing made in the scope and depth of available real-world information.Sophisticated database linkage and integration (page 4), fuelled by newapplications of information technology (page 18), has enabledunprecedented volumes of data for healthcare decision making, just asnovel techniques are extending its quality and utility for outcomesresearch (page 30).

These trends demand a very different mindset - one that moves beyondmore and deeper data towards better managing that data and focusingits use to maximize the value. In building a foundation for future growth,companies need new ways to collect and organize information, and toolsto help them turn this into a relevant body of evidence.

With over 230 multi-disciplinary experts focused on RWE solutions andHEOR globally, IMS is now, more than ever, finding innovative ways tohelp our clients succeed in this changing environment: we have centeredour approaches to data delivery on new client needs, leveraging thelargest collection of patient-level data and data-agnostic sourcing andacquisition to provide customized therapy area views; we have investedin technology to create true platforms that integrate data into common,linked data models; we have designed novel technology solutions to helpclients build, interrogate and analyze cohorts and visualize the results;and we continue to expand our expertise and capabilities as partners inresearch that creates the strongest evidence base for strategic decisions,value demonstration and engagement with a growing range of stakeholders.

I hope you will enjoy this issue of AccessPoint.

“The trends demand a very different mindsetcentered on better managing real-world data and focusing its use to maximize the value.”

Jon ResnickVice President and General Manager Real-World Evidence SolutionsIMS [email protected]

AccessPoint is published twice yearly by the IMS Real-World Evidence (RWE) Solutions and Health Economics & Outcomes Research (HEOR) team. VOLUME 3, ISSUE 6. PUbLISHED MAY 2013.

IMS HEALTH 210 Pentonville Road, London N1 9JY, UK Tel: +44 (0) 20 3075 4800 • www.imshealth.com/[email protected]

©2013 IMS Health Incorporated and its affiliates. All rights reserved.Trademarks are registered in the United States and in various other countries.


International experts bring new momentum to China’s move towards evidence-basedmedicine at 3rd CORE Summit in Shanghai.

IMS AsiaPac supports HTA in China at cornerstone regional meeting

Internationally, healthcare community interest inreal-world outcomes research and integratedhealthcare management has grown rapidly as aneffective measure of the clinical- and cost-effectiveness of medical interventions in largepatient population settings.

Specifically in China, the following trends are observed:

• Clinical experts are demanding additional data oncomparative effectiveness and patient-reportedoutcomes

• Social health insurance agencies are increasinglyrequesting evidence of treatment pathways as part ofthe reimbursement process

• China Ministry of Health and SFDA are strengtheningpost-market surveillance of medical products

• Healthcare professionals across China have accepted the concept of evidence-based medicine and areincreasingly demanding data on burden of disease and product use in the Chinese patient population.

STIMULATING EVIDENCE-BASED MEDICINE IN CHINAAs the only event dedicated to outcomes research, healthtechnology assessment (HTA), value-based healthcaremanagement and eHealth in China, CORE (China OutcomesResearch & Evidence-based Medicine) Summit has a key roleto play in stimulating and reinforcing evidence-basedmedicine in this country.

Organized jointly by China Medical Doctor Association – which has a reach of 2 million licensed medical physiciansin China – and Vital Strategic Research Institute, CORE servesas a critical platform for leaders from government, medicalsocieties, academia and industry to share their vision, policiesand knowledge in this area. In April 2013, more than 30experts from around the world were invited to the 3rd CORESummit in Shanghai to offer their perspective on the impactof real-world evidence (RWE), healthcare value definition,challenges and opportunities in chronic diseasemanagement, HTA, eHealth and big data.

INTERNATIONAL CONTRIBUTIONSAs one of the main event sponsors, IMS contributed to a pre-conference workshop on ‘Challenges in RetrospectiveDatabase Acquisition, Analysis & Reporting’, as well as givinga plenary talk on ‘Real-World Evidence: The Next Step’. This isthe second year running that IMS has been invited to speakat the meeting.

Day 1: Clinical outcomes measurement andoutcomes research

Key topics covered during the first day were data quality;methodological issues and best practice; incentivization forboth patients and clinicians/researchers; and stakeholderengagement and accountability.

Methodology and key findings were presented from studiesand registries around the world, including the NationalCardiovascular Data Registry (NCDR) run by the AmericanCollege of Cardiology, and China’s own CardiometabolicRegistry (CCMR), supported by the Chinese Ministry of Health.Dr Carolyn Clancy, Director of the US Agency for HealthcareResearch and Quality (AHRQ), described the Patient CenteredOutcomes Research Initiative (PCORI) and how putting thepatient at the center of chronic disease management couldbe the blockbuster drug of the 21st century – with the righteducation and engagement.

NEWS | HTA IN ASIA PACIFIC

IMS contributed to a workshop at the 3rd CORE Summit in Shanghai


HTA IN ASIA PACIFIC | NEWS

Another distinguished speaker, Mr John brooks, President,CEO of Joslin Diabetes Center, Harvard Medical School,outlined the use of smart phone technology in providinginformation that empowers patients to make the right lifestylechoices for managing their diabetes.

Day 2: Application of outcomes research andevidence-based medicine

The second day brought topics ranging from post-marketingsurveillance in the regulatory context to the use of real-worlddata for health policy and HTA in China. Some overridingthemes were the sheer size of China’s task in incorporatingevidence-based decision making as part of its reformprogram, as well uncertainty around the best way ofimplementing HTA to the optimum benefit of the Chinesehealth system. It was suggested that scientific evidence andguidelines alone are not enough for efficient clinical practice,and that the importance of communication anddissemination of evidence should not be underestimated.

Highlights included a keynote presentation by Jianying Guo,Deputy Director Pricing, China National Development andReform Commission, on the use of RWE as part of China’spricing and reimbursement policy development, and insightsfrom Professor Gordon Liu, Professor of Economics at PekingUniversity, on developments in health economic researchpolicy in China.

Joe Caputo from IMS AsiaPac overviewed current applicationsof RWE and the reasons why it has gained such global traction,

including examples from Asia. He also considered how bigdata, which will bring together electronic medical records(EMRs) with many other sources of medical, social andbehavioral data, will require organizations to think differentlyabout developing and implementing infrastructure and datamanagement frameworks so that information is readilyavailable to all stakeholders in the healthcare system.

CONTINUING THE MOMENTUMCORE has attracted increasing numbers of experts, speakersand delegates from within and outside China each year. Thiswas another successful meeting with excellent input fromleading experts showcasing examples of RWEgeneration/chronic disease management initiatives whilstaddressing the practical issues and challenges of collectingand utilizing such data. Availability and quality of data inChina may still be variable but it is clear that this country isready to progress the evidence-based healthcare agenda.That said, there are many practical challenges; by its verynature, the data reflect real-world practice and it would benaïve to expect the perfect dataset at this stage of theevolution. Rather, all stakeholders should be encouraged bythe interest and enthusiasm around the methodology anduse of such data to help improve healthcare quality andefficiency.

based on the success of this and previous CORE meetings, the2014 Summit will place more weight on the topics of eHealth,eClinical and the practice of using EMRs for measuring clinicaloutcomes, healthcare quality and patient engagement. •

First Health Policy Decision Makers Forum Asia Pacific, in SingaporeIMS sponsors independent platform for high-level dialogue to help disseminate innovative solutions in developedand developing co untries.

In November, 2012, the Institute of Health Economics and Management (IHEM), ESSEC business School gatheredexperts and delegates from Asia, Europe and US to address the challenges of making good healthcare available to allin the rapidly evolving landscape of Asia Pacific. Topics included an assessment of needs, resource issues, affordabilityand funding options, case study reviews, and access to efficient treatments. IMS will also be supporting a forthcomingESSEC executive training program in Singapore to help participants develop the knowledge and skills for customizingeconomic analyses to their countries.

For further information on IMS Health RWE Solutions & HEOR capabilities in Asia Pacific, please contact Joe Caputo [email protected]


Data linkage offers tremendous potential to study long-term outcomes of treatmentchoices in important diseases.

Integrated IMS PharMetrics Plus™ powers significant,wide-ranging new research opportunities

IMS Health is working with leading life scienceorganizations to build patient-centric integrateddatasets in therapy areas such as oncology,rheumatoid arthritis, multiple sclerosis, diabetesand rare diseases.

The ability to link disparate data sources brings the power togenerate richer and more detailed clinical information acrossthe entire continuum of patient care.

Through its recent collaboration with Health IntelligenceCompany, operating as blue Health Intelligence, IMSbiopharmaceutical clients have gained access to one of thelargest US health plan claims databases, adding to thecompany’s market leading PharMetrics Plus™ database.

The aggregated dataset comprises adjudicated claims formore than 150 million unique enrollees across the UnitedStates. Enrollees with both medical and pharmacy coveragerepresent more than 42 million unique lives in 2011 andmirror the US age distribution of the US population under 65years. In sample disease areas for 2011 this patient coverageincludes:

• Hypertension: >4.5 million

• Migraine: >550,000

• breast cancer: 250,000

Among key information enabled by PharMetrics Plus aregreater patient segmentation, availability of 3-digit zip-code,patient out-of-pocket payment, hospital discharge status aswell as additional inpatient and provider-level detail.

Data can be linked with all IMS US patient-level data assets,including elecronic medical records (EMR), laboratory, hospitalcharge master and consumer data as well as clients’ own data (eg, from registries or clinical trials) (Figure 1). We are currentlyapproaching a total of 50 million lives available for linking with internal and external data, and this number is scheduledto grow.

EXPANDED RESEARCH POTENTIAL PharMetrics Plus paves the way for significantly expandedhealth economics and outcomes research opportunitiesincluding:

• burden of illness across settings of care

• Cost and resource utilization studies

• Medication adherence, persistence and compliance studies

• Comparative effectiveness analyses

• Patient treatment patterns and treatment flows

• Patient and provider segmentation

• Epidemiological prevalence and incidence analyses

• Pharmacovigilance and safety studies

The database is supported by a unique and proprietaryalgorithm for data linkage based on de-identification ofpatients, ensuring compliance with HIPAA1 regulations, as wellas an algorithm for the creation of unique patient IDs anddeterministic matching.

NEWS | IMS PHARMETRICS PLUS™

fIGURE 1: INTEGRATING PATIENT-LEVEL DATA WITH PHARMETRICS PLUS

PharMetrics Plus150m US total

patient lives

Hospital

Lab Data

Oncology/SpecialtyEMR

AmbulatoryEMR

Customer Data

Consumer OTCMedication

Consumer Behavioral & Demographic Data

Open-Source Medical & Pharmacy Claims

1 Health Insurance Portability and Accountability Act


IMS PHARMETRICS PLUS™ | NEWS

EXTENDED DATA LINKAGE OPPORTUNITIESbeyond the data linkage possibilities within IMS, there isfurther potential for customers to link their own patient-leveldata – from clinical trials, prospective observational studiesand disease registries – to IMS data assets, subject to patientprivacy agreements. The data feed is processed from sourceby a trusted third-party contractor, leveraging a HIPAA-compliant de-identification process, to provide a singular viewof the patient experience across multiple channels (Figure 2).

Patient-centric data warehouseIMS data can also be linked to external sources of patient-leveldata such as EMRs from specialty physician practices. A patient-centric data warehouse has been created linkingIMS US data to external claims and registries. The resultingplatform contains more than 500,000 patient records withenhanced client value gained from links to death records,tumor registries and medical claims. Work on this platform hasformed the basis of 30 publications in the 5 years since itsinception. The knowledge gained from this process is beingapplied to the integration of all PharMetrics Plus datasets. •

fIGURE 2: HOW DOES IMS LINK DATA AND MAINTAIN HIPAA COMPLIANCE?

OVERVIEW Of IMS US PATIENT-LEVEL DATABASES

Hospital

Ambulatory EMR

Oncology/Specialty EMR

Lab Data

Consumer OTC Medication

Consumer Behavioral and Demographic Data

Open-Source Medical and Pharmacy Claims

Customer Data

Patient Patient

HIPAA-compliant de-identification

process

PATIENTA single patient interactswith the healthcaresystem across multiple entry points

MULTIPLE CHANNELSEach interaction is capturedat the de-identified patientlevel in the same manneracross channels

DE-IDENTIFICATIONAll interactions arecombined upon thecommon algorithm inplace across channels

PATIENTA singular viewof the patientexperience isrepresented

IMS Health US APLDdata source Linkable Size*

Health Plan Claims ✓Adjudicated claims for over 150M uniqueenrollees in the US. Enrollees with bothmedical and pharmacy coverage representmore than 42M unique lives in 2011

Medical Claims ✓Over 150M patients, 1B claims and 3Bservice records obtained annually frommore than 800,000 providers

Pharmacy Claims ✓Over 120M patients, 1.6B prescriptions,capturing ~40% of all US prescriptionsclaims

Ambulatory EMRs ✓ 23M patients with data from 40,000physicians

Oncology EMRs ✓ 650,000 patients across 370 locations ofcare

Hospital Charge DataMaster ✓ 113M encounters annually from more than

650 hospitals

Laboratory Test Results ✓ 40% of all outpatient laboratory testsconducted in the US

Consumer Behavioral andDemographic Data ✓

Consumer information on ~130M patients,1M physicians and 150,000 physicianpractices

Consumer Over-the-Counter Medications ✓ Data collected from ~40M households

*Size indicative of stand-alone dataset; matching across datasets varies. Forprofiling inquire at IMS Health.

For further information about IMS PharMetrics Plus orfor data inquiries, please contact Shibani Pokras [email protected]


IMS Institute for Healthcare Informatics expands understanding with a meaningfulperspective for optimized performance of medical care.

New studies analyze dynamic trends in key areas of US health system

The IMS Institute for Healthcare Informaticscontinues to support its research agenda withinsights to help improve the quality and cost ofhealthcare delivered. Findings from three of its latest publications identify thedynamics driving change in the use of medicines, Managed Medicaid, and e-Prescribing prevalence.

DECLINING MEDICINE USE AND COSTS: FOR BETTER OR WORSE?This new review of the use of medicines in the US in 2012shows a market in a state of flux. Trends are marked by adecline in drug spending to US$325.7 billion for the firsttime ever – suggesting that patients are self-rationing dueto the bad economy – and explosive growth of genericsreflecting double the level of patent expiries from 2011. Theanalysis also highlights the highest number of newmedicine approvals in 15 years with many breakthroughsavailable for the first time that will potentially affect over 20million patients. As the Affordable Care Act fundamentallychanges access to healthcare in the US, the IMS Instituteconsiders why the landscape will continue to change, andwhy spending on medicines is expected to remain belowoverall healthcare expenditure levels in the next 5 years(Figure 1).

SHIFT FROM FEE-FOR-SERVICE TO MANAGEDMEDICAID: WHAT IS THE IMPACT ON PATIENT CARE? Managed Medicaid is seen by many States in the US as away to deliver better care at lower cost; recent actions toreduce use of Fee-for-Service plans have been significant.This new review explores early trends in prescription drugutilization in four States that have switched their pharmacybenefit to Managed Medicaid. Comparing changes in theuse of antipsychotic, respiratory and diabetes medications,it identifies some areas of impact, including increased costsavings when pharmacy and medical benefits are offered inthe same plan, but wide differences in the magnitude of thechanges between States and disease areas. Highlighting thesubstantial and growing importance of Medicaid, thefindings also point to the value of care coordination servicesto monitor and encourage the appropriate use of medicines.

E-PRESCRIBING PREVALENCE:HOW EXTENSIVE AND HOW INTENSE?Electronic prescribing is recognized to be an increasinglysignificant driver of pharmaceutical prescriptions. Focusingon the cholesterol-lowering market, this analysis leveragesretail prescribing data across both 'traditional only'prescribers and e-Prescribers to consider the extent andintensity of e-Prescribing within the US healthcare providerpopulation between 2011 and 2012. It offers insights intochanges in the volume of e-Prescribing and in the numberof e-Prescribers, together with a perspective on meaningfuluse levels and implications for growth in e-Prescribing. •

Further information on these reports and other insightsfrom the IMS Institute can be accessed from the Institutewebsite at www.theimsinstitute.org, together withinformation on its extensive range of research activities.

NEWS | IMS INSTITUTE INSIGHTS

Source: National Association of State Budget O�cers, State Expenditure Report, 2010-2012; Congressional Budget O�ce Source: National Association of State Budget O�cers, State Expenditure Report, 2010-2012; Congressional Budget O�ce

Higher Education All other spendingDefense Elementary & Secondary EducationMedicaid Social Security

US Federal Budget 2011 Total of State’s Budgets 2011

$3.6 Trillion $1.6 Trillion

Medicines SpendingHealthcare Spending

10%

5%

0%

-5%2002 2007 2012 2017

Growth

fIGURE 1: REAL PER CAPITA SPENDING GROWTH 2002-2017

Source: CMS National Health Expenditures Jul 2012; IMS Health, National Sales Perspectives, Dec 2012; US Census Bureau Jan 2013; Economic Intelligence Unit Nov 2012; IMS Market Prognosis Apr 2013


HTI-CPRD unlocks research potential across GP and secondary care

NEW LINKED DATASET | NEWS

Unique linkage of retrospective longitudinal datasets completes patient pathway inEngland for robust decision support.

Responding to market demand to understand thevalue of medicines using real-world evidence(RWE), IMS Health has announced the availabilityof HTI-CPRD. This retrospective longitudinalpatient dataset enables the entire patienttreatment pathway in England to be mappedacross both major sites of care.

The development follows the June 2012 launch of HTI(Hospital Treatment Insights) and the expansion of a strategicpartnership between IMS and the CPRD (Clinical PracticeResearch Datalink) in September 2012. HTI links hospitalactivity with drugs dispensed to create a longitudinal patientrecord in the hospital setting in England.

FASTER, MORE ACCURATE DECISION SUPPORTProviding a window on the true standard of care, the HTI-CPRD retrospective dataset can be interrogated to answercritical questions around patient diagnosis, treatment andoutcomes. It is anticipated to have value for decision makersboth within and outside of England, enabling more robustsupport of value messages, informing the cost vs benefitdebate, aiding investment decisions for new indications,responses to safety concerns from regulators, and preparationof research to share with payers.

Today, coverage within the linked primary and secondary caredataset has reached ~208,000 unique patients who can befollowed longitudinally back to January 2010, and thisnumber continues to grow. •

For further information on HTI-CPRD and its ability to inform the complete patient journey in England, please contact Joshua Hiller at [email protected]

Hospital Pharmacy Audit

Clinical PracticeResearch Datalink

Hospital Episode Statistics

Diagnosis

Initial GPconsultation

GPconsultation

Readmission

Secondary Care

Primary Care

Discharged Operation

Drugdispensed

Drugrepeat

Drugrepeat

Repeatprescription

Drugrepeat

Drugdispensed

Change indrug regimen

Outpatient visit

Hospital TreatmentInsights

Patient drug record

Primary CarePatient drug outcomes

Patient outcomes

INSIGHTS | REAL-WORLD EVIDENCE PERSPECTIVE

Increased evidence demands for market access have driven majoradvancements in patient-level datasets and the technologies to increase valueto the growing number of data users. but how can this vast foundation of databe leveraged to improve efficiency and value? We ask IMS expert Jon Resnick forhis perspective on what it will take to realize the true potential of a growingbody of real-world evidence (RWE).

Jon Resnick, MBA is Vice President and General Manager RWE Solutions, IMS Health [email protected]

The interviewee

PAGE 8 IMS REAL-WORLD EVIDENCE SOLUTIONS & HEOR

Why focus is the key to unlocking the value of real-world,evidence-based data

Q: What has intensified the need for RWE?JR: We are hearing a lot about RWE at the moment but it has actually been around in some form or another for a longperiod of time. Health economic and outcomes researchers have been generating this type of information for decadesto support prescriber and payer decision making. What has changed in the last several years is the timeframe overwhich RWE is being used to determine the value of a product. In Europe, we have seen much greater emphasis oncurrent agents as health systems try to deter the rapid uptake of many innovative medicines. Similarly in the US, RWE – the data collected outside of clinical trials – is already being leveraged by payers and providers to better informtreatment decisions. What was once a race to achieve pricing and market access has now become a process ofcontinued scrutiny by all healthcare stakeholders across the product lifecycle, creating the need for new and expandedreal-world data and different types of study endpoints.

Q: How have data sources been evolving to meet this new demand for RWE?JR: There have been significant developments, both in the magnitude and reach of commercial patient-level databasesas well as the availability of the data. We have started to move beyond siloed datasets to sophisticated linkage andintegration of multiple data sources, allowing a more holistic picture of patient treatment across different sites of care.At the same time, initiatives such as the OMOP (Observational Medical Outcomes Partnership) in the US, and EU-ADR(Exploring and Understanding Adverse Drug Reactions) in Europe are making patient-level data more publicallyavailable, for example, to monitor drug safety and for signal detection. For pharma, the resultant growth in data meansboth new opportunity and complexity. These data assets, when coupled with technology and the skilled resources toexecute the analytics, offer a new mechanism to make business decisions, articulate product value and engagestakeholders through new service offerings. On the other hand, these datasets and required analytics are incrediblycomplex. At IMS, for instance, we have patient-level data in more than 260 million patients in the US alone across morethan eight linkable assets. The range of analytic questions that these start to address across pharma stakeholders canbe dizzying.

Q: How far has the market come in using RWE? JR: It is quite surprising how extensively RWE is being used today. IMS has spent the last few months compiling auniverse of studies and we have been amazed by the number of examples we have found. Just using literature searchesand informal discussions, we identified more than 100 products globally where RWE has had a measurable impact oneither initial or sustained market access. For the most part, these were high-profile products from large drug classeswhich had generic or low-cost alternatives that were being traded against branded products in the comparisons. Thereare some fairly prominent examples from companies and payers across Europe and the US, as well as a complete mix ofstudies. These include payer-sponsored analyses examining specific drug classes, pharma/payer collaborative initiativesto look at particular products and disease areas, and sometimes pharma-sponsored work that has been presented. So,already, we are starting to see tangible results from its use.

REAL-WORLD EVIDENCE PERSPECTIVE | INSIGHTS

Where next for RWE?

continued on next page


What was once a race to achieve pricing and market access has now become a process of continued scrutiny by all healthcare stakeholders across the product lifecycle.

INSIGHTS | REAL-WORLD EVIDENCE PERSPECTIVE


Q: How are different countries performing in collecting real-world data?JR: We’ve looked at this globally at IMS and compared markets across several dimensions: availability and access todata; standards and approaches to using the data that are systematic to a market; and applications and decisionmaking. What we’ve found first and foremost is that if you look across markets on a 20-point scale, no market scoresabove 11. This means we are only about halfway there on a scale of sophistication as to how RWE can be used. What wefound on a relative basis is that several markets are doing well against some of those dimensions. For example, in theUK, there is phenomenal access to data. The NHS has done a very good job in terms of making that informationavailable and although the systematic frameworks aren’t quite there yet, there is now a strong foundation forconducting good research in this market. The US scores well in having a lot of commercial availability of data, but thefragmented nature of the market makes it challenging to integrate this in a very seamless way. but the challengesacross countries are very different.

Q: What is your view about the progress being made?JR: I think these are still early days in terms of the types of analysis that can be done in linking, for example, aspects oftreatment like adherence to cost-effectiveness. There is much more that can be achieved going forward as RWEexpands in the domain. but already, it is a practice that everyone is using in some way to evaluate products, and themore we scrutinize definitions around it and develop standards and consistent methodologies, the better it will beunderstood and utilized to support good decision making.

Q: What are the hurdles to achieving this objective?JR: There is a need for scientific methods to improve understanding of how to work with the data and raise trust in andacceptance of real-world information. A major challenge, too, is navigating privacy regulations. Although personal datacan be leveraged in a fully anonymized manner for the greater good, specific markets are less advanced in workingthrough the detailed issues to make this a reality. One of the biggest challenges, though, is stakeholder alignment andwillingness to share the data across different settings. Collaboration is going to be key to ensuring that technologiesand data pockets can connect. From our perspective, the only way to encourage this discourse is open, honestdiscussion around strong, rich data with good methodologies. This is where we enter the marketplace, aggregatingdata, linking different datasets and bringing things together in a safe and secure way to facilitate that stakeholder trustand collaboration.

Q: How can the full potential of real-world data be realized?JR: I think this will take effort on several fronts. Firstly, through continuing to work with the data and make meaningfulconnections by pulling together fragmented information and joining individual data streams. Physicians, providers,payers and regulators are all looking for information to help them make better decisions to improve health outcomes.but no one is accessing a solid foundation of evidence. It is by linking all this information into an evidence base that wecan drive greater efficiency and value.

The other area that will be increasingly important is more focused and efficient use of the data to support changingneeds over time. We now have a huge wealth of information which continues to expand in both scope and volume.While this significantly improves our ability to demonstrate and articulate the value of products, it also calls for newskills and capabilities in pulling from this broad reach of data to ensure that the right evidence is created for the rightstakeholders. Real-world data has the power to drive discussions across the entire healthcare setting – with regulatorsto inform market access decisions, with physicians on which patients are likely to benefit most from different types oftreatment, and with managed care to demonstrate how companies can add value to the ongoing care of their patientpopulations. The ability to support this dialogue with relevant, focused evidence will be essential to improvingefficiency and quality in healthcare.

REAL-WORLD EVIDENCE PERSPECTIVE | INSIGHTS


Q: How is IMS helping to move this agenda forward?JR: As a third party that is independent of product creation and the decision-making process, we are committed tosupporting the advancement of evidence-based healthcare and promoting dialogue across decision makers throughthe use of real-world research. We are working with our customers, as well as key stakeholders, to understand theirstrategic needs and help them build the tools and capabilities to leverage the information to assess value, ensure safetyand drive healthcare efficiency.

We have been expanding and linking our real-world data assets – the recent launch of IMS PharMetrics Plus™ hascreated the most comprehensive patient-level database in the US for a complete picture of the patient pathway acrosssettings of care. This has brought tremendous new analytic possibilities for outcomes research. In Europe, we arecollaborating with payers and providers to integrate IMS assets with external data sources to provide the scope anddepth of information needed. The recent linkage of IMS hospital data to the CPRD primary-care dataset, for example,has provided the means for following patients across the full treatment journey in England.

As companies face the challenge of handling ever increasing amounts of data, we have also been building technicalplatforms to help them organize and leverage this information in a cohesive way. These include data warehouseshosting customized datasets, catalogues of profiled data to optimize searches, and advanced analytics librariesallowing users to analyze disparate data using standardized statistical methods. New applications and visualizationtools, such as IMS Real-World Explorer, are enabling the creation of population cohorts through a user-friendlyinterface. And engagement platforms are providing the means to share and communicate the research. Together, thesesolutions are enabling customers to more strategically manage and better use their data assets for RWE generation.

Q: What is the outlook for RWE?JR: We are already starting to see a move towards sharedresponsibility in not only conducting good analyticsaround the information base that’s there but also helpingthat to impact patient care. Our own collaboration withAstraZeneca reflects a shared perspective on thetransformative power of RWE on global health systems.The incentives are coming around, people understand therole we all need to play and things are moving forward. I am sure that, with industry commitment, five years fromnow the role of RWE will be secure, methods will befurther advanced and trust will have grown. •

IMS RWE TECHNOLOGY SOLUTIONS

Real-world reportingApplications to standardize, share and communicate research

Analytic workstationUser-friendly RWE research tools and disease models

Data catalogueLibrary of profiled datasets to optimize search

Data harmonizationLinked de-identified data warehouses to host customized datasets

Real-World ExplorerCohort builder and visualization to simplify and govern analytics

Advanced analytics librariesPowerful analytical libraries for deep insights

and advanced and standardized statistical methods

Client RCT data

Client observ.data

IMS claims, EMR, LRx data

Third-partyassets

RWE lens for pharmaSee our Insights feature overleaf for new IMS findings on the marketimpact of RWE.

I am sure that, with industry commitment, five years from now the role of RWE will be secure, methods will be further advanced and trust will have grown.

IMS HEALTH ECONOMICS AND OUTCOMES RESEARCH

INSIGHTS | REAL-WORLD EVIDENCE IMPACT

Despite the growing importance of real-world evidence (RWE) as a basis forstakeholder decision making, pharmaceutical manufacturers have struggled toact on this trend. New research from IMS offers quantifiable and actionableinsights for informed and focused RWE investments within the context ofprevailing dynamics.

Ben Hughes, PHD, MBA, MRES, MSC is Senior Principal RWE Solutions, IMS [email protected]

Marla Kessler, MBA, BS is Vice President and EMEA Leader, IMS Consulting Group, IMS Health [email protected]

The authors


OUTCOMES - Issue 1 Page 1

An international comparison of the use and impact of real-world evidenceThe pharmaceutical industry’s increasing focus on RWE reflects the greater supply ofelectronic patient-level data and higher stakeholder demand for RWE-baseddecisions. So why have manufacturers struggled to act? They have been constrainedby a limited fact base and isolated case examples, prompting this research detailingdata supply dynamics and over 100 cases studies of actualRWE influence on product decisions. These quantifiable insights debunk a number of common beliefs:

• RWE’s influence on decisions about medicines has increased in magnitudeand scale in western markets: more than 100 observed case studiesillustrate its evolution beyond pharmacovigilance (PV)

• Payers have applied RWE in assessing value in a variety of ways, includingexpanding medicines use where warranted: cost containment is not the sole objective

• Although payers are a powerful stakeholder in setting the RWE agenda,proactive pharma engagement matters: manufacturer-generated RWEinfluenced over 25% of observed decisions

• RWE strategies need a local context but four fundamental marketarchetypes can focus pharma efforts: pharma does not need a uniqueapproach in every country

This study provides a detailed understanding of market dynamics, consolidatingthem into four dominant archetypes. It enables manufacturers to focus RWEinvestments via improved internal alignment and gain greater value fromstakeholder engagement. The insights it provides are also relevant to policymakers and payers seeking value from RWE.

THE STRUGGLE FOR DECISIVE ACTIONThe increasing need to obtain better value for healthcare spend has elevated RWE’s importance as a decision-makingtool. This is particularly true for medicines. In separate research, IMS estimated that improved medicines’ use couldavoid USD300-650 billion of cost worldwide.

Even stakeholders who see the RWE opportunity and its increased use struggle to act on it. Limited, isolated public casestudies create a narrow picture of how RWE has affected decisions, and misjudge the complexities in the underlyingdrivers of RWE across markets. The debate is muddied further by different stakeholder perspectives – industry versuspayers, health economics versus pricing and market access (PMA).

REAL-WORLD EVIDENCE IMPACT | INSIGHTS

RWE market impact on medicines: A lens for pharma





The result is confusion, misalignment and even organizational paralysis over what to do about RWE in pharmaceuticalcompanies. Some see RWE narrowly – supporting safety or mandatory submissions – while others see a broad lever toengage stakeholders. While RWE evangelists clash with skeptics wanting proof that RWE matters, they themselves aresplit between those who see many positive opportunities and those focused on using it to mitigate risks.

FACT-BASED INSIGHTTo forward the debate, IMS sought an objective demand and supply lens on RWE. This focused on licensed medicinesuse rather than innovation, PV, or broader payer and provider use such as patient pathway evaluation, and engagedpayers, health technology assessment (HTA) experts and clients in over 50 interviews.

To characterize demand, more than 100 non-safety examples were identified in which RWE impacted medicines (Figure 1).Mainly driven by payers, RWE has influenced license (label), access, pricing, and use across countries and therapeuticareas (TAs). Approximately 25% of these decisions reflect industry-generated RWE, demonstrating that pharmaceuticalcompanies influence this evolving landscape.

In addition to demand, real-world data (RWD) supply was examined, focusing on database use rather than (costly)prospective data generation. A proxy for supply, RWE output through peer-reviewed research varied from the thousandsof publications in the US and UK to only a few hundred in Germany and France. This difference reflects varyingusefulness of electronic data and different stakeholders’ ability to access it. Useful data would have extensive coverage,illustrate the full patient journey and have high clinical depth and quality. While only selected actors might need thislevel of data to create value, in practice widespread appropriate access generates more research output (Figure 2).

Overall, no country has an ideal data supply, with usefulness of or access to data constraining supply to differentextents. In access-led countries (above the arrow) improving supply focuses on usefulness, such as the UK’s nationalCPRD1 linkage program. Companies play a role, too, such as IMS’ US strategy of developing sophisticated HIPAA-compliant2 linkage technology. In usefulness-led countries (below the arrow), debates are ongoing to improve access,such as to payer data in France. Meanwhile, individual companies are engaging directly with physicians and patients forconsent to lever data for research.

fIGURE 1: RWE APPLICATION CASE STUDIES fIGURE 2: RWE SUPPLY fROM DATABASES

Approximately 25% of these decisions reflect industry-generated RWE, demonstrating that pharmaceutical companies influence this evolving landscape.



This new demand-supply fact base is not completewithout market-making mechanisms or frameworks.Enabling demand, these detail how decision makingincludes RWE. Examples include evaluation mechanisms(eg, HTA, reimbursement processes, clinical guidelinedevelopment), dissemination, and measurement (eg, prescriber incentives, payment-for-performance,quality indicators). This complete framing can helppharmaceutical companies, payers and policy makersalike derive the fuller potential of RWE.

RELATIVE MARKET COMPARISONTo highlight markets’ individual characteristics, theaforementioned drivers were translated into an RWEassessment scale – data supply and demand frameworkswere each scored out of five, and application was scoredout of ten reflecting the importance of RWE demand in practice.

This reveals major differences in RWE impact, with countries scoring between 2 and 11 of a potential 20 (Figure 3). The maximum score of 11 reflects that no country has the ideal conditions for RWE use in a scalable manner andhighlights RWE’s infancy. Lower scores indicate that RWE is relatively less available or more costly to generate with less consistent or transparent use in decision making. but even in markets with lower scores, RWE is still relevant.

In terms of data supply, the US scored highest, with a commercial market ensuring data availability for research needs,enabling research output greatly surpassing other countries. The US did not score a maximum five as ongoing linkageefforts are yet to achieve their potential, and underlying Electronic Medical Record (EMR) data capture is lower relativeto other countries. Conversely, countries such as Spain score low given limited pockets of usable data.

On frameworks, the UK is closest to the ideal because RWE is used in systematic review for most evaluation processes(HTA, reimbursement, clinical guidelines). Stakeholders can disseminate RWE directly to prescribers, and RWE-enabledpayment-for-performance contracts encourage appropriate prescribing.

Even the UK can go further: for example, RWE-enabled prescribing indicators are still limited. Conversely, countries likeDenmark and Spain lack clearly defined roles for RWE in decision frameworks.

In terms of application – where RWE has informed decisions – all countries are distant from the ideal, with little consistentuse of RWE in transparent decision making across TAs or patient populations. Case studies from the UK suggest the mostextensive application, given the number, variety and breadth of resulting decisions relative to the entire health system.Conversely, in countries such as Germany public case studies of RWE application are rare.

MARKET CLUSTERS AND STRATEGIESThe analysis explains markets through RWD supply and RWE demand (clear frameworks and application to decisions).These dimensions and scores segment markets into four groups: Pioneers, Traders, Explorers, and Laggards (Figure 4 overleaf).

PioneersStakeholders in Pioneer markets use their relatively notable RWD supply to inform drug decisions. Countries in thisgroup – the Netherlands, Sweden and the UK – all have strong national HTAs, suggesting an impact from concentrateddecision making. In these markets, pharmaceutical companies should set high ambitions for RWE plans, demonstratingvalue and engaging stakeholders based on a variety of real-life views (eg, disease, product, class, cross care settings,long-term outcomes, payer-relevant quality of care indicators). They must fully exploit RWE beyond traditionalevaluations to enable commercial strategy, leverage outcomes-based marketing, and use innovative evidence toolswith local health systems.

fIGURE 3: RWE MARKET IMPACT SCORES


For example, one ground-breaking manufacturerdeveloped a mobile (iPad) evidence platform to support itsdiabetes drug launch in the UK. It used RCTs and variousRWD sources to build models of prescribing patterns, costand outcomes for general practices. Trained salesrepresentatives engaged prescribers by adjusting the pre-loaded parameters of the model (eg, patient numbers,prescribing profile, cardiovascular risk factors) to discussprescribing from the clinician’s perspective. As RWEbecomes an accepted dialogue with payers and cliniciansin Pioneer markets, companies without these capabilitieswill be disadvantaged against or unable to respond tomore innovative competitors.

TradersThe US is the only country representing the Traders,though other countries not in scope could have a similarmodel. Owners of RWD share it without stipulating how itshould be used beyond ensuring individual privacy.

Most US insurance companies and providers sell data and only use it to support specific analyses about their ownpopulations. Thus, pharmaceutical companies have broad data access to drive performance, from trial design throughcommercial support. Successful US strategies involve evidence platforms and tools that support multiple internalstakeholders. However, without clear interpretative frameworks, the channels for external engagement are morenuanced. Only selected payers engage readily on RWE, and FDA regulations on RWE dissemination are more restrictivethan in Pioneer countries. A differentiated engagement approach is needed, requiring creative thought and investment.

One leading company, for example, developed a rich platform in one priority TA rather than a ubiquitous platformacross TAs. Over several years it linked all relevant datasets (Rx, Claims, EMR, registries, RCTs, observational studies,biobanks) and developed different internal customer tools to exploit it. This asset supports traditional RWE and multiplepeer-reviewed publications. More impressively it generates hundreds of internal standard reports, even improving salesforecasting accuracy. This capability enhances external engagement too, as the manufacturer is now a reference forlocal prevalence estimates or for characterizing local unmet patient needs.

ExplorersExplorers, such as France and Italy, have a significant demand-side vision but limited RWD supply. France’s bold visionincludes using RWE for cost-effectiveness assessments and regular class reviews without detailing how extensive datain the health system can be accessed or levered. In Italy, there is widespread use of payment-for-outcomes or coveragewith evidence development, but how these schemes inform coverage or pricing decisions based on the captured dataremains unclear. While manufacturers can react to these limited demands for RWE, the more innovative ones mightplace a bet that the markets will expand RWE use over time. There is no crystal ball, but this RWE demand could signalan evolution to pioneer-style markets. Either way, pharmaceutical companies must develop some RWE capabilities forpayers’ current focus areas.



As RWE becomes an accepted dialogue with payers and clinicians in Pioneer markets,companies without these capabilities will be disadvantaged against or unable torespond to more innovative competitors.

fIGURE 4: MARKET SEGMENTATION fOR PHARMACEUTICALINDUSTRY STRATEGIES



For example, one inventive company has “gambled” on developing a high quality evidence platform in France. With noaccess to payer data, it has partnered with commercial vendors to use innovative and cost-effective approaches tomaximize the value of existing EMR and Ministry of Health datasets. Using these as an initial platform, the company isgathering supplementary data to develop a high quality reference cohort in a chronic disease. In addition to classicRWE, this generates process-of-care indicators, setting a benchmark for understanding patient outcomes.

LaggardsFinally, there are the Laggards who may use RWE more in future, but face significant hurdles today. The Laggards in thisstudy are Canada, Denmark, Germany and Spain, all of which illustrate different challenges (eg, strong data privacy,fragmented healthcare landscapes). In these markets, pharmaceutical companies benefit from engaging directly withselected stakeholders willing to lead on RWE. Markets with strong regional payers may see that leadership from thoseregions, such as Cataluña in Spain or Ontario in Canada. In Germany, some sick funds have expressed willingness topartner on RWE.

Given the limited resources of these stakeholders and the large number of manufacturers, developing a clear valueproposition and local RWE capabilities are essential to becoming a preferred partner. Innovative companies have longbeen in dialogue with regional stakeholders, quietly making co-investments in research capabilities to further allparties’ goals.

FROM INSIGHT TO ACTIONHow can senior executives lever these insights for actionable RWE strategies? The emphasis and insights can be used toengage their teams to determine:

• Where will additional investments in RWE create most value for our portfolio (eg, market types, TAs, stakeholders,phases of the lifecycle)?

• What changes to brand evidence plans or stakeholder engagement approaches on evidence can capture the RWEpotential in each of the four market types?

• How should our RWE-generation capabilities be reinforced, such as scalable platforms, targeted stakeholderengagement, or deployment of innovative channel tools?

While franchise and brand teams naturally drive questions on the where, increasing leadership from PMA is required onthe what, as is leadership from HEOR, epidemiology and other evidence functions on the how. Senior executives mayneed to personally champion cross-functional RWE discussions given the strategic issues involved and givenorganizational hesitancy around perceived RWE risks even at the expense of potential gains. •

For a full view of country-by-country dynamics, detailed case studies and methodologies, please request the extended white paper from the authors.

1 Clinical Practice Research Database2 Health Insurance Portability and Accountability Act

AcknowledgmentThis work has been a collaborative effort from many individuals across a range of backgrounds and settings. The authors sincerelythank the contributions from numerous payers, clients, members of the global RWES, HEOR and Consulting teams at IMS and othersfor their knowledge and expertise, without which these insights would not have been possible.


INSIGHTS | IMS SYMPOSIUM

As demand for real-world evidence (RWE) continues to grow, new innovations ininformation technology are enabling more sophisticated use of real-world datathrough linkage and harmonization. An IMS Symposium at the ISPOR 15thAnnual European Congress in berlin explored the tangible value for outcomesresearch and progress towards the ultimate goal of international interoperability.

Jacco Keja, PHD is Senior Principal RWE Solutions & HEOR, IMS [email protected]

Ian Bonzani, PHD is Engagement Manager RWE Solutions, IMS [email protected]

The authors



The driving force of information technologySeveral factors are fuelling the need for real-world evidence (RWE) on a much largerscale, not least the growing demand for more accurate and timelier drug safetyevidence information. In the US, the Observational Medical Outcomes Partnership (OMOP) is leading the development of infrastructure andmethods to support the FDA’s Sentinel safety initiative, with a focus on the linkage of multi-source observational data.Similarly, in Europe, the EU-ADR project is developing an innovative system for the earlier detection of adverse drugreactions by pooling clinical data from the electronic medical records of millions of patients in the region – leveragingmodern biomedical informatics technologies.

Other factors driving requirements for bigger, deeper, connected real-world data include the need for more efficaciousmedicines, based on better understanding of patient segmentation and the link between outcomes and geneticvariability, as well as increasing use of payment-for-performance arrangements and value-based pricing. These demanda greater level of granularity than that afforded by traditional retrospective research focused on claims and medicalrecords, with a population-based approach being the ultimate goal.

The move away from single datasets towards interoperable data and analysis platforms integrating and linking datafrom multiple sources has many benefits for health outcomes research and healthcare delivery, enabling the creation ofa clearer, more comprehensive longitudinal patient journey across disease and treatment pathways of interest.

THE VISIONLooking to the future of healthcare delivery in 2020, it is possible to envision a world with access to truly integratedclinical trial and real-world data, supported by the technologies to facilitate its regular use in the healthcare decision-making process (Figure 1 overleaf ).

This is a world of broader engagements between healthcare stakeholders where information is the currency of theseexchanges. A world where, in real time, appropriate care options can be examined and assessed across a variety ofdifferent disease and patient characteristics; where optimal treatment options can be identified across these differentmetrics and translated effectively into more integrated and holistic healthcare choices tailored to individual patient anddisease area needs; a world where the right patients have access to the right treatments at the right time with the bestopportunity to maximize outcomes and minimize disease severity and risk.

In this world, healthcare stakeholders can evolve, becoming collaborators in a system focused on individual patient careand shared decision making. Patients can move from being largely passive information receivers to more proactivedecision supporters; providers can change from being implementers of guidelines and policies to more empoweredvehicles for delivering quality and efficiency in healthcare; payers can evolve from being holistic population risk-benefitmanagers to being more active around patient and disease areas and point of care interventions; and pharma cantransform from the role of medicine providers to being true collaborators in the healthcare delivery space.

IMS SYMPOSIUM | INSIGHTS

Powering a new age of outcomes research



The move away from single datasets towards interoperable data and analysisplatforms integrating and linking data from multiple sources has many benefits for health outcomes research.



DRIVERS OF EVOLUTIONThe shape and pace of these evolutions are being drivenby several factors which are increasing the ability toprocess, access and link real-world data. These include theestablishment of methods and analytical standards, thelegal and ethical framework for governance of data usage,and activities to build stakeholder acceptance and trustbased on evidence transparency.

At their heart is information technology (IT) and theapplications that fall within it. IT is the engine that willdrive the success, powering, for example, the building ofintegrated research platforms based on the best practicemethods and standards being developed by dataconsortia such as OMOP and C-DISC, and guidelines frombodies such as ENCePP and the PCORI. It will also enablethe development of infrastructure firewalls andgovernance layers wrapped around patient data toaddress and mitigate some of the concerns over legalitiesand patient privacy.

KEY AREAS OF INNOVATIONA deeper dive reveals six core areas of innovation that areprogressing quite quickly. These include emergingapplications of natural language processing and theability to extract more useful information from patientnotes or discharge letters; federated research approachessuch as SHRINE, MAELSTROM/DATASHIELD, and de-identification/linkage engines to address nuances arounddata access, governance and integration; predictivemodeling and smart learning systems, including use ofartificial neural networks in oncology clinics enabling thefaster, more accurate diagnosis and prediction of patientoutcomes across various tumor types; systems to powerthe processing speed and engines behind all thedevelopments; and visualization tools to packageinformation in more useful and valuable ways (Figure 2).

fIGURE 1: HEALTHCARE 2020

fIGURE 2: INNOVATION COMES fROM KEY TECHNOLOGIES THATIMPROVE THE CREATION & UNDERSTANDING Of EVIDENCE

Visualization toolsCare and patient �ow diagrams, patient P&L…

Predictive modeling and smart systemsNew and learning algorithms to better diagnose patients and predict patient outcomes…

Data integration (Linkage, ETL, Harmonization)Patient De-ID engines, harmonization/validation rules, data marts…

Structure to unstructured Natural language processing, sentiment analysis, signal processing, association rule learning…

New range of dataEHR, genomics, biobank, social

media/sentiment, PROs…

System processingDistributed computing, open-source software

development…

EHR, genomics, biobank, social New range of data

EHR, genomics, biobank, social New range of data

diagrams, patient P&L…Care and patient �ow Visualization tools

media/sentiment, PROs…EHR, genomics, biobank, social

diagrams, patient P&L…Care and patient �ow Visualization tools

media/sentiment, PROs…EHR, genomics, biobank, social

association rule learning…analysis, signal processing, analysis, signal processing, processing, sentiment Natural language Structure to unstructured

association rule learning…analysis, signal processing, analysis, signal processing, association rule learning…

processing, sentiment Natural language Structure to unstructured

Predictive

outcomes…predict patient patients and better diagnose algorithms to New and learning smart systemssmart systemsNew and learning

modeling and

development…open-source software

Distributed computing, System processingSystem processing

Distributed computing, predict patient

better diagnose algorithms to New and learning smart systemsmodeling and

development…open-source software

Distributed computing, System processing rules, data marts…

harmonization/validation Patient De-ID engines, Harmonization)(Linkage, ETL, Data integration

rules, data marts…harmonization/validation Patient De-ID engines, Harmonization)(Linkage, ETL, Data integration

Appropriate care options

Patient-tailored healthcare delivery

Prescribing decision support of the future

E-detailing forum

Patient 1 Patient 2Characteristics

ManufacturerSpecialistsupport Network of GPs

Epidemiology, Risk Factor, Disease stage, Genomic pro�le X

Optimal treatmentStandard of care

CharacteristicsEpidemiology , Risk Factor, Disease Stage, Genomic pro�le Y

Optimal treatmentRx A coupled to services B

Most appropriate: Drug treatment & dosing regimen Integrated supportive care services (eg, home

care/monitoring, nurse outreach, remote link-ups to GPs/specialists) Patient education and empowerment programs




E-detailing forumtorsupp

ecialistSp anufacM

E-detailing forumsk of GPorwetNerturanufac

Patient 1

o�le Xprenomic G, Disease stageortacF

, REpidemiologyeristicstacharC

Yo�le prenomic Gtage, Disease SortacF

Epidemiology , RharC

o�le X, , Disease stage

isk har

ed of cartandarStmeneaptimal trOo�le Xprenomic G

es Bvicsero oupled tRx A c

ttmeneaptimal trO Yo�le prenomic G

ttmeno�le X


Patient education and empowerment programslink-ups to GPs/specialists) care/monitoring, nurse outreach, remote Integrated supportive care services (eg, home Drug treatment & dosing regimen

Most appropriate:



Most appropriate:


IMS SYMPOSIUM | INSIGHTS


These technologies are not only improving the ability to collect, curate, link and use patient-level information but arealso then enabling that data to be translated into a holistic package of evidence that can be moved closer to thedecision-making front line to enable more informed choices. However, it is the way in which they converge andintegrate that their true value becomes apparent in facilitating the outcomes research of tomorrow:

• Driving RWE value to wider groups of business and healthcare decision makers

• Enabling more accurate health economic models based on real-world populations and evidence

• Translating short-term endpoints to long-term outcomes

• Allowing deep patient segmentation/characterization of patients

• Avoiding under/over-estimations of appropriate use of Rx, events, signals

Figure 3 shows some of the applications that are emerging to support the change process. Externally, these includetools to aid HTA or better simulate real-world use and risk-benefit; more advanced point-of-care management to helpphysicians and pharmacists make more accurate and informed decisions regarding individual patient care; real-timerisk-sharing/payment-for-performance platforms; and the ability to design more continuity in stakeholderengagement. Underlying these is a range of integrated and mechanized applications for optimizing internal workingspace, in terms of real-time benchmarking and KPI tracking against quality indicators and being able to driveefficiency and incentivize those indicators.

IMPLICATIONS FOR INDUSTRYFor pharmaceutical companies, the evolution of RWE has placed new demands on the information generated duringresearch and development. Increasingly sophisticated types of RWE are required to demonstrate how new drugs willperform in specific healthcare settings, with evidence requirements now extending right through to lifecyclemanagement. RWE has a critical role to play in improving clinical development through understanding treatment andoutcome diversity, in minimizing decision uncertainty, and in creating a “learning healthcare system” throughperformance indicators, information and incentives. It will also benefit patients long-term by enabling the industry todevelop more targeted, value-added medicines. However, the creation of effective RWE requires the right data andthe right technology to access, integrate and analyze these data. Innovative, integrated ‘big data’ networks, such asthose being developed by AstraZeneca and IMS through their RWE collaboration, will enable a much clearerunderstanding of unmet need and better articulation of the value of medicines.

fIGURE 3: TECHNOLOGICAL INNOVATION IS ALSO POWERING APPLICATIONS fOR OPERATIONAL AND CLINICAL CHANGE

EXTERNAL CHANGE SUPPORT

Consultative reimbursement/valuesupport tools

• HTA validation/support

• CER/H2H simulation dashboardsPoint-of-care management

• Advanced CPOE to support care decisions

• Health surveillance & responseReal-time risk sharing

• P4P systemsEngagement platforms

• Provider/Plan selection tools (“TripAdvisor”)

INTERNAL CHANGE SUPPORT

ULTIMATELy BETTER PATIENT OUTCOMES & MORE COST-EFFECTIVE HEALTHCARE DELIVERy

Integrated & mechanized researchplatformsKPI/QI tracking & benchmarkingR&D optimization:

• Advanced CTO tools/Signalvalidation/Asset investment validation Appropriate care options



E-detailing forum

Patient 1 Patient 2Characteristics

ManufacturerSpecialistsupport Network of GPs

Epidemiology, Risk Factor, Disease stage, Genomic pro�le X

Optimal treatmentStandard of care

CharacteristicsEpidemiology , Risk Factor, Disease Stage, Genomic pro�le Y

Optimal treatmentRx A coupled to services B

Most appropriate: Drug treatment & dosing regimen Integrated supportive care services (eg, home

care/monitoring, nurse outreach, remote link-ups to GPs/specialists) Patient education and empowerment programs




E-detailing forumtorsupp

ecialistSp anufacM

E-detailing forumsk of GPorwetNerturanufac

Patient 1

o�le Xprenomic G, Disease stageortacF

, REpidemiologyeristicstacharC

Yo�le prenomic Gtage, Disease SortacF

Epidemiology , RharC

o�le X, , Disease stage

isk har

ed of cartandarStmeneaptimal trOo�le Xprenomic G

es Bvicsero oupled tRx A c

ttmeneaptimal trO Yo�le prenomic G

ttmeno�le X



Most appropriate:



Most appropriate:





DATA HARMONIZATION EFFORTS UNDERWAYHarmonizing and linking data across multiple cohorts bring the potential to expand scientific knowledge based onvery large sample sizes and synthesized information. Many new cohorts are collecting high-quality data, and abroader range of data is being made available through linkage to health registries or environmental exposureregistries.

In the US, for example, the NUgene gene-banking project at Northwestern University in Chicago is targeting thecollection and storage of DNA samples and longitudinal medical information from more than 100,000 patients tohelp understand the genetic mechanisms behind common diseases. In Europe, bbMRI-ERIC (biobanking andbiomolecular Resources Research Infrastructure-European Research Infrastructure Consortium) is working to improvethe accessibility and interoperability of bio-samples from various populations across the region. And research centerssuch as KORA in Germany and CARTaGENE in Canada are generating novel scientific knowledge through researchplatforms based on the pooling of data. At the same time, organizations such as Obiba and LifeLines are developingmethods, software and expertise to support the synthesis of information in different research areas as well asharmonization and linkage of data. Projects such as ENGAGE (European Network of Genetic and GenomicEpidemiology) and HALCYon (Healthy Ageing across the Life Course) and bioSHaRE (biobank Standardisation andHarmonisation for Research Excellence in the European Union) will make use of the data collected and the toolsdeveloped in order to generate that new scientific knowledge.

BioSHaREWork conducted in the bioSHaRE project illustrates some of the practical issues in harmonizing data. Led by ProfessorRonald Stolk from the Netherlands, this aims to develop specific tools in collaboration with the Maelstrom ResearchPlatform to create harmonization and standardization in the use of pooled data from different cohort and biobankstudies. Together they are developing a series of software applications to cover all the building blocks for pooling andsharing data across cohorts, through a series of sample projects.

The success and value of harmonized analysis of collaborative research across heterogeneous studies depends notonly on access to high-quality data but also on rigorous methodological approaches and on the specificdocumentation of the data. Knowing exactly which harmonization variable has been used and where the data havecome from is key to enabling reproducibility of the results that are generated.

PROGRESS TOWARDS POSITIVE CHANGEFew would question that the path towards data interoperability is a challenging one – not least in managing theidiosyncrasies of real-world datasets, in improving internal efficiencies and resource allocation decisions, and inunderstanding the implications for achieving credibility with decision makers. However, there is growing recognitionthat the path exists and that the challenges are not insurmountable. It is by overcoming these through the use oftechnology, that the potential to drive operational and clinical change across healthcare settings can be realized. •

This article draws on presentations from the IMS Symposium “Information technology: Powering the next generationof outcomes research and healthcare delivery”, held during the ISPOR 15th Annual European Congress in Berlin,Germany in November, 2012. Chair: Jacco Keja, PhD, Senior Principal RWE Solutions & HEOR, IMS Health. Speakers: IanBonzani, PhD, Engagement Manager RWE Solutions, IMS Health; Andrew Gaughan, MSc, Global Director, Payer and RealWorld Evidence Informatics, AstraZeneca Pharmaceuticals; and Isabel Fortier, PhD, Researcher, Research Institute of theMcGill University Health Center and Director of Research and Development for the P3G Consortium. A copy of the fullproceedings can be obtained by emailing Angelika Boucsein at [email protected]

GLOBAL FOOTPRINTINFORMATION ON HUNDREDS OF MILLIONS OF ANONYMIZED PATIENTS

PATIENT-LEVEL INSIGHTS | IMS


Real-world patient insights. Real impact.IMS LifeLink™ is a global source of comprehensive real-world patient information andsolutions.

LifeLink delivers more powerful insights into the patient experience that will helphealthcare stakeholders improve business performance, patient outcomes and quality of care.

LifeLink offers a powerful way to discover real-world patient insights:

• Information assets that track hundreds of millions of anonymized patients around the world

• Proven real-world patient analytics and insights to address scientific and commercial applications enterprise-wide

• Delivery tools and customized consulting services for clients’ unique business needs

LifeLink provides an extensive library of powerful analytics for game-changing real-world results:

• Industry-leading methodologies and technologies ensure accurate and reliable linking of disparate datasets

• Consistent and comparable views across time

• Highest level of patient data privacy

IMS LifeLink PharMetrics Plus™ is the largest, most diverse integrated US healthplan database available. With the broadest coverage of geographies, care settingsand industries, it provides data on more than 150 million US patients and allows fordata integration with IMS patient-level data as well as clients’ own and external data.

CANADA• Longitudinal Rx

• Drug Plan Claims(Oncology, hospital)

EUROPE• Longitudinal Rx

(belgium, France, Germany, Italy,Netherlands, Sweden, UK)

• Electronic Medical Records(France, Germany, Italy, UK)

• Oncology Analyzer(France, Germany, Italy, Netherlands,Spain, Turkey, UK)

• Hospital Disease Database(belgium)

• Hospital Treatment Insights(UK)

• Longitudinal Patient Database(Sweden)

ASIA PACIFIC• Oncology Analyzer

(China, Japan, South Korea,Taiwan)

• Longitudinal Rx(Japan, South Korea)

AUSTRALIA• Longitudinal Rx

UNITED STATES• Longitudinal Rx

• Health Plan Claims

• Electronic Medical Records(Oncology, ambulatory)

• PharMetrics Plus

• Hospital Charge Detail Master

INSIGHTS | ONCOLOGY REAL-WORLD DATA

Saeed Noibi, MPH is Consultant RWE Solutions & HEOR, IMS [email protected]

Karin Berger, MBA Principal RWE Solutions & HEOR, IMS [email protected]

David Bertwistle, PHD is Senior Consultant RWE Solutions & HEOR, IMS Health [email protected]

The authors


Data sources suitable for oncology comparative effectiveness research arelimited and heterogeneous in Europe. As reliance on observational studiescontinues to grow, a basic prerequisite for a successful real-world evidence(RWE) strategy is the systematic identification, evaluation and prioritizationof accessible real-world data.

Identifying the most appropriate sources for specific research needsEnsuring future reimbursement for oncology drugs is dependent on high-qualityclinical data to generate convincing evidence of comparative effectiveness foralternative treatment options. The particular demands on evidence requirements for oncology drugs have been documented in previous IMSresearch.1 Relative to other disease areas, randomized controlled trials (RCTs) in oncology do not always demonstratereal clinical endpoints and adverse drug reaction profiles. These evidence gaps are most profound in rare oncologyindications, due to the very limited number of cases combined with the frequent use of personalized treatment plans.Observational studies using patient-level clinical practice data with large sample sizes and long-term follow-up areincreasingly relied on to meet data and evidence needs.

TRENDS TOWARDS OBSERVATIONAL RESEARCHIn the US, population-based data sources, such as administrative claims data, are already used to generate evidence inoutcomes research. In Europe, evidence requirements from a manufacturer’s perspective include characterization ofunmet medical need across different countries, estimations of the size of target study populations, and determinationof disease pathways and relevant endpoints.

Recent advances in data technology enabling the linkage of different data sources to create complete patient pathwaysare becoming an important real-world data (RWD) capability. The reliability of this approach depends on data attributesthat allow for the most precise linkages based on unique patient identifiers across multiple data sources (deterministiclinkage). Therefore, data sources with these attributes are especially valuable for real-world evidence (RWE). Forexample, linkage of pharmacoepidemiology databases and cancer registry data sources with unique patient identifiersin the respective sources facilitates high-quality research which is not achievable in the individual data sources. Thehuge surge in healthcare data generated for varied purposes has enabled the evolution of platforms for RWD andevidence generation. International research collaborations and pan-European legislation provide a real opportunity toshape the landscape for evaluating, accessing and utilizing these data sources for health technology assessments(HTAs) in particular, and health policy research in general.

beyond Europe, the benefits of harnessing ‘big data’ generated in the clinical care of cancer patients has started to yieldfruitful discussions in the wider oncology community. The American Society of Clinical Oncology (ASCO) has recentlylaunched an initiative to link the multitude of electronic medical records (EMR) from different clinical practices andmalignancies with the purpose of generating aggregate data to inform clinical practice and inform physicians on real-world patterns of care.2 This initiative exemplifies the need for credible RWD and sets the pace for the European clinicaloncology community.

Gaining access to large retrospective data sources in Europe for pharmacoepidemiology research is still challengingwhen compared to the US. Until recently, population-based cancer registries were the major source of cancer data forhealthcare policy research and cancer surveillance in Europe. However, due to their primary purpose of estimatingcancer incidence and prevalence, these registries have limited usefulness for outcomes research. Furthermore, the issueof patient confidentiality continues to pose restrictions in accessing the data source.3 Discussions on the use ofretrospective observational studies have now started in earnest in European countries.

INSIGHTS | ONCOLOGY REAL-WORLD DATA ONCOLOGY REAL-WORLD DATA | INSIGHTS

Oncology real-world data in Europe





CATEGORIZING ONCOLOGY RWD SOURCES IN EUROPE To better understand and characterize the European landscape of patient-level data in oncology, IMS has undertaken acomprehensive evaluation of RWD sources in the region. When mapped against the needs of different stakeholders,this reveals potential gaps where future investments and initiatives could optimize the RWD environment for oncology.The process followed is outlined below.

1. Systematic data source identificationThe study began with a detailed literature search of peer-reviewed papers, published in the MedLine and EMbASEdatabases, to identify RWD sources used in epidemiology and outcomes research. In order to increase search sensitivity,search strings included therapy area of interest and studies conducted in Europe while excluding interventional studiessuch as RCTs.

Since many RWD sources of emerging interest extend beyond those traditionally used for research studies, an extensivesearch of the grey literature was also conducted (Figure 1). The efficiency of this search was facilitated by anunderstanding of which literature types would likely contain the data sources of interest. Databases cataloguingdifferent data sources were also reviewed to fill any potential gaps. These included the “ISPOR Outcomes ResearchDigest”4, b.R.I.D.G.E To Data5, and the ENCePP repository.6 Finally, to supplement the desk research and identify anyfurther sources of data, an interview program was undertaken with key opinion leaders (KOLs) who had publishedworks on outcomes research.

fIGURE 1: AN EffICIENT GREY LITERATURE SEARCH PROCEEDS WITH CAREfUL CATEGORIZATION Of LITERATURE TYPES fOR RWDSOURCE IDENTIfICATION

Grey literaturesearch

strategy

Internationalnetworks/collaborations

Multi-country prospective datacollection

Retrospective/data re-useinitiatives

Regional/country-wide datainitiatives

Patient groups/non-governmentalorganizations data

‘Ice-breaker’ in the use of non-conventional sources

Local research data initiatives

Country-specificdata sources

Proceedings/Abstracts and otherresearch sources

Search terms included:• “Cancer”• “Registries”• “Research databases”• “EMRs”• “Longitudinal research” • “Prospective databases”• “Retrospective databases”

broad-base grey literaturecategories

Reference to primary/secondary researchdata/evidence


ONCOLOGY REAL-WORLD DATA | INSIGHTS


TABLE 1: EVALUATION CRITERIA APPLIED IN THE SCREENING PROCESS

Database GeneralName WeblinkAdministrator contact detailsScope (Regional, National, International)Scope (Country/countries)Brief description of databaseOwner of database

Database AccessConditions for accessData feesAccess to full database or restricted to specific data cutsPossibility of licensing data or requirement for collaborative research group

Characteristics of Data SourceData type (eg Audit, Biobank, Claims data, Electronic health records,Epidemiologydatabase, Registry)Coverage (Indications, eg general health records, oncology, hematologicalmalignancies, etc)Patient population sizeStatus (Ongoing or complete)First data availableMost recent updateFrequency of data accrualLinked to other data sourcesLinkage capability

Patient DemographicsAgeEthnicityGender

Clinical DataDiagnosesDate of diagnoses Details of diagnosisSymptoms at diagnosisComorbidities Diagnoses recurrenceDate of recurrenceDetails of recurrence (eg, Ipsilateral/contralateral, local/regional, etc)

Treatment DataDates of treatments Treatment lineTreatment regimenTreatment response Treatment durationAdverse events

Lab DataLab tests recordedDates of lab tests recordedResults of lab tests recordedResource Use & Unit CostsResource useCost

OutcomeMortalityDate of mortalityCause-specific mortality

2. Screening for relevant data sources Due to the variety of RWD sources, screening criteria wereadopted to ensure that only data sources of relevancewere considered for further evaluation. The mostimportant criterion was that sources must containpatient-level data rather than population-basedaggregate data. Furthermore, previous experience hasshown that some patient-level data initiatives may havebeen discontinued due to inadequate funds, terminatedprojects or logistic reasons. While data sources that are nolonger accruing data may still be useful in evidencegeneration for descriptive- and hypothesis-generatingstudies, contemporaneous data sources provide insight tomore recent health technologies and advancements inclinical practice. For this reason, sources where dataaccrual had stopped over a period of time were notincluded for further evaluation.

Another benefit of including contemporaneous dataconcerns the duration of disease cycle and clinicalpathway. Indications with longer disease pathways orwith the likelihood of recurrence after a long period ofremission will require data sources with continuous andlongitudinal data. A critical element of the evaluativeprocess was to understand the data variables (metadata)and the values that constitute the data. This description isan integral part of well-maintained data sourcedictionaries. While established data sources have defineddata dictionaries or some form of metadata repository,many other sources do not have these dictionaries readilyavailable to external researchers or stakeholders.

3. Evaluation of screened data sources Data sources that were screened into the analysis, whichrepresented just under half of those originally identified,were subsequently evaluated using criteria which takeinto account the particular indications of interest. Thisinvolved reviewing data dictionaries where available, aswell as interviewing data owners to elucidate the types ofdata collected within the data sources. Table 1 shows thecomplete list of the evaluation criteria that were applied.

IMS REAL-WORLD EVIDENCE SOLUTIONS & HEORPAGE 28 IMS REAL-WORLD EVIDENCE SOLUTIONS & HEOR



4. Prioritization of data sources In order to identify optimal data sources with dataattributes that would facilitate the fulfillment of differentevidence needs, a prioritization framework was applied(Figure 2). Evaluated data sources were plotted on theframework based on the level of attributes collected andthe conditions for access (access models). Considerationof data-linkage capabilities and the privacy laws indifferent European countries was a critical part of thisprocess. Through the use of the framework, only 12% ofthe original data sources were identified for prioritization.

LANDSCAPE OF ONCOLOGY DATA SOURCES IN EUROPEThe evaluation revealed a paucity of data sources valid for the purposes of oncology RWD research in Europe. As shownin Table 2, no more than one fifth of those identified contained relevant information in terms of treatment-basedattributes (20%), outcomes data attributes (19%) and linkage capabilities (17%).

Overall, the rigorous end-to-end process of identification, screening, evaluation and prioritization found that only 5% ofthe original pool of data sources had the potential to become accessible pan-European real-world oncology datasources. Furthermore, the study highlighted six countries as being best positioned to drive the oncology RWDlandscape in the region: Denmark, Finland, Germany, the Netherlands, Sweden and the UK (Figure 3).

fIGURE 2: DATA PRIORITIZATION fRAMEWORK TO INfORMINVESTMENT DECISIONS fOLLOWING EVALUATION Of DATASOURCES

Assessment of accessLimited Moderate Significant

Dat

a at

trib

utes

cov

ered

Lim

ited

Mod

erat

eSi

gnifi

cant

Deprioritized: Data sourcesthat can be dropped fromany medium-long-termbusiness plan

Potential: Data sources that requirefurther analysis and prioritizationwhich can be included in the long-term business plan

Optimal: Data sourceswith potential quick-winsfor which business casescan be developed

TABLE 2: CURRENT ESTIMATE Of ONCOLOGY RWD LANDSCAPE IN EUROPE

Real-world data research capability Percentage of data sources* Number of countries

Data sources with treatment data attributes 20% 10

Data sources with outcomes data attributes 19% 14

Data sources with linkage capability 17% 12

*Not cumulative percentage as some data sources fall under more than one category.

Identifying the most valid data sources for individual research requirements is an essential part of evidence generation but demands both time and resources.

ACCESSPOINT • VOLUME 3 ISSUE 6 PAGE 29ACCESSPOINT • VOLUME 3 ISSUE 6 PAGE 29

ONCOLOGY REAL-WORLD DATA | INSIGHTS


fIGURE 3: END-TO-END ONCOLOGY REAL-WORLD DATA REALIZATION PROCESS

The end-to-end process involves a diverse IMS RWE Solutions team that includes a wide range of deep, in-country expertise

Data sourcesidentified(100%)

National data linkage

IMS perspective onEuropean countrieswith potential todrive oncology RWDlandscape in Europe

Systematic andstructured grey literaturesearch

19 17 17 10 6

Data sourcesscreened(83%)

Types of data sources (Not exhaustive)

Data sourcesevaluated (45%)

Data sourcesprioritized(12%)

Pan-European realizationof accessible data sources(5%)

Surveillance

AuditAudit

Health surveys

National data linkage

National data linkageNational data linkage

Research databases

Registries Research databases

Research databasesResearch databases

Hospital registry

Hospital registry

Hospital registryBiobank

EMR

EMR

EMR

EMRBiobank

Biobank BiobankData mart

Dataharmonization

1 berger K. Oncology growth drives new evidence needs: The special demands on cancer treatment. IMS Health AccessPoint, 2012; 3(5): 12-152 Transforming Cancer Care through big Data: ASCO Unveils CancerLinQ Prototype. Accessed 27 April 2013 at http://www.asco.org/transforming-cancer-care-through-big-

data-asco-unveils-cancerlinq-prototype3 Izquierdo JN, Schoenback VJ. The potential and limitations of data from population-based state cancer registries. Am J Public Health. 2000 May; 90(5): 695-698 4 International Society of Pharmacoeconomics and Outcomes Research (ISPOR) Outcomes Research Digest. Available at

http://www.ispor.org/research_study_digest/index.asp5 The search engine for healthcare databases. Available at http://www.bridgetodata.org/6 European Network of Centres for Pharmacoepidemiology and Pharmacoviligilance (ENCePP) Database of Research Resources. Available at www.encepp.eu

CONCLUSIONSIdentifying the most valid data sources for individual research requirements is an essential part of evidence generationbut demands both time and resources. These are critical factors to consider when planning RWE strategy. The variationin healthcare systems and confidentiality laws across Europe is perhaps the biggest challenge to RWD andobservational research, alongside variation in data standards and quality in the region. At a broad level, much willdepend on the success of ongoing EU and national initiatives to drive the creation of a harmonized framework for datacollection and the implementation of real-world studies.

Notwithstanding these challenges, multi-factorial solutions do exist to shape the RWD landscape in Europe. Theseinclude a flexible and pragmatic approach to address the variety of data source types available. Sophisticated dataplatforms and data marts are already enabling more efficient integration of multiple datasets and providing the meansto interrogate and harness much larger banks of real-world data more effectively and efficiently. Ultimately, transparentcommunication of mutual values led by credible researchers and KOLs will be important to fostering collaboration anda comprehensive data governance platform to access and generate oncology evidence from RWD in Europe. •

INSIGHTS | NATURAL LANGUAGE PROCESSING

As the use of patient-level databases continues to expand, creative approaches to overcoming their limitations are significantly extending their quality andutility for real-world research. Here we describe a novel coding technique, using natural language processing (NLP), which has nearly doubled availabledata on key measures reported in IMS LifeLink™ EMR Disease Analyzer France.


Massoud Toussi, MD, MSC, PHD, MBA is Principal and Medical Director, RWES & HEOR, IMS [email protected]

The Author

Practical insights from IMS LifeLink™ EMR Disease AnalyzerFranceHealthcare databases are unique in their ability to capture relevant and timelyinformation on drug prescribing and effectiveness in conditions of real-worldpractice across large sample patient populations. Varying in value and application, these datasets are considered to be of high quality if they are fit for use in theirintended operational, business and scientific role.1 When a gold standard exists, “fit for use” can be interpreted ascompliance of the data with that standard in a number of ways. As shown in Figure 1, data properties such as relevance,accuracy, timeliness, comparability and completeness can be used to define the quality of a database.

One of the major limitations of electronic medical records (EMR) is lack of completeness. In the case of a primary-careEMR database, this can arise for several reasons:

• Physicians are often short of time and fail to record inthe EMR all the information they receive on a patient.

• Physicians who do register clinical information in theEMR tend to do so in their own free-text wordingrather than by using coding systems such as the ICD 10(WHO International Classification of Diseases) or ICPC(WONCA International Classification of Primary Care).

• Even among physicians who are willing and trained touse coding systems, in some areas, such as laboratoryobservations, there is lack of harmonization in terms ofhuman usable coding systems.

• A significant amount of information on a patient, suchas hospitalization reports and referral letters, is by itsnature composed in free text and is not exploited indatabases due to technical or data privacy issues.

Thus, one of the ways of overcoming the limitation of incompleteness of the data is to code the free-text informationand transform it into structured, exploitable information, thereby improving its value.

NATURAL LANGUAGE PROCESSING Major EMR databases often contain millions of patient records, representing tens of millions of lines. To manually codeall the free-text information contained within these records would require significant resources. Natural languageprocessing (NLP) is an approach that allows automatic coding of free-text information in a fraction of the time it wouldtake to achieve by hand. Moreover, NLP is a sustainable solution: once an NLP engine is developed and plugged into anEMR database it can be re-run periodically, for almost no incremental cost, to code any newly added free text.

NATURAL LANGUAGE PROCESSING | INSIGHTS

Optimizing EMR database value using natural language processing



fIGURE 1: QUALITY INDICATORS Of A DATABASE

Timeliness(data still

useful)

Accuracy(fewer errors)

Comparability(identifying

fields)

Relevance(appropriate

data)

GoldStandard

Completeness (no missing

records/variables)


NLP is a field of computer science, artificialintelligence and linguistics concerned with theinteractions between computers and human(natural) languages. The history of NLP dates backas far as the 1950s and today it is widely used inthe processing and routing of letters (scriptrecognition), speech recognition applications,mobile phones and search engines. Itencompasses a wide range of techniques and canbe implemented using various commercial andfree, open-source tools.

The principle behind NLP is that a humanunderstandable text or speech is analyzed bycomputer against a knowledge base. Thisknowledge base can be a dictionary of terms withcodes related to each term, with the objective ofmapping free text to those codes (eg, ICD Codes).

As shown in Figure 2, an NLP project for an EMR database typically involves the following steps:1. Understanding the process of transforming free text to codes

2. Understanding the free-text data to be coded

3. Preparing the data to be coded:• Text data preparation• Sampling of the text data into two text corpuses: one for testing and one for implementation

4. Developing a knowledge base:• Dictionary (or lexicon) of ordinary terms with their variations• Lexicon of technical information for use in coding containing all linguistic variations of the coded information

5. Modeling:• Correction of spelling and grammar errors• Identification of text pieces (tokens) which could be considered as valuable• Identification of codes which correspond to the above tokens (bridging)

6. Evaluation:• Performance measures and repetition of iterations until achievement of acceptable accuracy

Through the following project example, the different steps of NLP for coding data from an EMR database are discussed,together with the challenges that can arise, especially in the case of a non English-language EMR.

A CASE IN PRACTICE: IMS LIFELINK EMR DISEASE ANALYZER DATABASE FRANCE The hurdles that may be encountered in practical implementation of NLP have been demonstrated in a project usingIMS LifeLink™ EMR Disease Analyzer Database France (DA). This is a longitudinal database containing the electronichealth records of patients treated and followed-up by a representative panel of about 1200 GPs in France since the early2000s. The database contains 4.5 million patient records comprising patient demographics, prescriptions and diagnoses.

At the commencement of the project, one of the limitations of DA was its lack of laboratory values due to theunavailability of coded data. In order to address this issue, 49 million lines of free text recorded in the field “Laboratoryexam label” and 53 million lines of free text in the field “Laboratory exam value” were identified in the database. Abouttwo thirds of this data had been already bridged by traditional methods but lacked preciseness and validity. It wastherefore important to re-bridge the entire corpus.


fIGURE 2: A TYPICAL NLP PROJECT PROCESS

1. Coding processunderstanding

2. Understanding

of free text

3.Preparation and

cleaning of free-text data

4.Knowledge base

development

5.Modeling

6.Evaluation



PRACTICAL CHALLENGES 1. Text languageOne of the main challenges of NLP projects is that they are very much language dependent. In the DA project, althoughseveral commercial and free open-source solutions existed for the standard English and French languages, no toolcould be identified for use in retrieving laboratory values from GP texts in French. Thus, it was necessary to create one.

The tool was developed using Python programming language. This is one of the most commonly applied computerlanguages for text processing due to its various libraries and features (most of the Google search engine is written inPython). It also has the advantage of being under a wide open-source license which makes it usable and distributableeven for commercial applications.2

2. Text corpusUnlike ordinary text corpuses, such as web blogs, forums and even twitter messages, where text is accompanied by aminimum of context and background, the information of interest in EMRs is not always in the form of complete,understandable sentences. In DA, the text was written in short-hand form, probably due to physicians’ lack of time fortranscribing laboratory values or other observations in complete phrases or even words. More specifically, eachphysician used his or her own short-hand language and layout for recording information. For example, for “cholesterolLDL to total cholesterol ratio”, a variety of writings could be found, such as “LDL/tot”, “LDL/chol”, “LDL to chol”, “LDL cholrat”, etc, none of which are common expressions that can be found in a standard lexicon.

In addition, the shorthand used by the doctor was in many cases very personalized, making it difficult to understandeven by other colleagues. For example, the letter “p” was variously used by different physicians for “patient”, “plasma”,“pulse rate”, “pain”, “purulent”, “polymorphonuclear”, “perimeter”, “pathogen”, etc. Thus, it was decided to develop a rule-based engine on the top of the lexicon to deduce from the context the right intended meaning (see below).

3. Encoding problemsA further challenge of NLP projects is the encoding ofcharacters in the text. Indeed, in DA, when data is sentfrom the physician’s software to the core database, the freetext can be encoded differently based on the operatingsystem and the software used by the physician. Somecharacters are transformed into non alphanumericalcharacters, making it impossible even for advanced spell-checker modules to work correctly (Figure 3). During theproject, this phenomenon happened even morefrequently because of the French letters and the highnumber of Macintosh users in the DA panel. It wastherefore necessary to develop a specific module tocorrect these encoding problems before implementingthe spelling checker module.

4. LexiconsDictionaries and lexicons of relevant domains are notalways available in all languages. For the DA project, alexicon of general and medical French language words wascreated from different resources found on the internet. Theresult was a dictionary of more than 500,000 French words,rich in medical terminology, which is available for use infuture projects and even commercial products.

However, a comprehensive commercial or non-commercial lexicon of laboratory observations can be difficult to find, even through medical dictionaries. In thisinstance, it was even more challenging to identify one for French laboratory observations. In the absence of such aresource, a book of laboratory values with about 1700 pages was implemented and almost the entire book coded tocreate a dictionary of coded terms.


fIGURE 3: EXAMPLE Of ENCODING PROBLEMS


5. Spelling correctionSpell checking is another key task in NLP. before any tentative bridging can commence, it is essential to make sure thatthe words to be checked in the database are correctly spelled – particularly given the spelling skills of many doctors!

The purpose of the spelling correction process is to identify words that do not exist in a dictionary and to guess theword which was intended. The objective is to correct lexical "non-word errors", ie, those where the word is misspelled.Omitting this step can mean the loss of useful information which potentially can be retrieved from the database.

Several studies have shown that misspelled words are generally close to their correct form.3 Typically, the Damerau-Levenshtein distance is applied to guess the closest correct word for one that has been misspelled. In this case, theminimum “cost” necessary for transforming word “a” to word “b” is calculated using the four following operationsdescribed by Damerau:4

• Insertion: "cholesterool"

• Deletion: "cholestero"

• Substitution: cholezterol "

• Inversion: "cholestreol"

(+ transposition or substitution pairs)

For the purpose of the DA project, a module was developed to calculate this distance for all of the words of the corpusthat did not exist in the dictionary, to find the appropriate word guessed. Each word had to be compared to the word inthe dictionary. In each case, this involved several thousand comparisons – a very processor-consuming procedure.

6. BridgingOnce these steps had been completed, bridging could begin. bridging consists of finding words expressed byphysicians in the lexicon and suggesting the appropriate code for them (here a unique form of the word). While thisprocess can be the simplest step in a standard NLP project, in the case of an EMR database it poses a particularchallenge due to the extremely private nature of medical records. Indeed, physicians often consider the free text theyenter into the EMRs to be personal notes needing only to be understood and de-coded by them. Consequently, theexpressions and layout forms used tend to be unique to each physician.

To resolve this, contextual intelligence was used in the bridging algorithm which applied the rule-based enginepreviously described. This engine allows the same word or phrase to be interpreted using evidence from the context ofthat word. For example, to identify whether ‘p’ in “blurred p”, stands for “plasma”, “patient” or “pulse rate”, both theadjacent words to “p” and the other uses of “p” by the same physician should be considered.

7. EvaluationIn an NLP project, not all the database is used for bridging. Generally, a random sample of a few thousand lines isprepared as the learning corpus and another sample is prepared for testing. All the processes described above are thusrepeated over and over until the required level of sensitivity and accuracy is obtained. Then, the NLP is implemented onthe overall database and, if necessary, enhanced again through iterations.

What should be the size of the initial sample? Nobody knows exactly. It depends on the variety of the text that is beingprocessed and also the capacity of the computers used during the development phase. In the DA project, it wasdecided to sample 30,000 lines of laboratory observations and as many for values. The evaluation of the right coding ofthese lines was done by human intervention through inspection of the codes and the programs were adjusted aftereach evaluation cycle.


NLP can bring tremendous benefits to a database and increase its value by providing new information for a very low cost.


The preparation of data, spelling correction, lexicon development, bridging and contextual intelligence was thusworked on by iteration until 97-98% of precision in the bridging was reached and it was certain that non-bridged textwas not bridgeable, even manually. Once the entire algorithm was considered acceptable in terms of sensibility andaccuracy, it was applied to the whole database and further enhanced.

MEASURING THE VALUE OF NLPIt is important to define metrics to evaluate the gainobtained through the NLP project and to be able toquantify its added value. In the DA project, a total of 47million lines of free-text laboratory exam labels (out of 49million) and 53 million lines of free text laboratory examvalues (out of 53 million) were bridged (Figure 4). Inpractice, the overall gain is always more than this plainincrease in the number of bridged records. In this case,more than 20 million lines of laboratory values which werealready bridged were not linked to identifiable laboratoryexam labels, and were thus not usable. With the NLPproject, those values were linked to their correspondingnewly bridged labels, forming groups of “laboratory examlabel + value + unit” which can actually be used inresearch.

As a practical illustration in one disease area, immediatelyprior to implementation of the project, HbA1C wasavailable for 8.6% of patients with diabetes mellitus. After completion of the NLP project, this percentage was as high as16.5%, thus almost twice the previous rate of available information.

COST-EFFECTIVENESS AND SUSTAINABILITYNLP can bring tremendous benefits to a database and increase its value by providing new information for a very lowcost. The DA project was conducted entirely within the framework of the six-month internship of a student studying fora Master’s degree in Computer Science and Linguistics.

Finally, a further advantage of an NLP project is its sustainability. In the case of DA, the whole cycle of the NLP wasautomated, so that the chain of the programs is run automatically every month to bridge the newly entered data intothe database. •

AcknowledgmentWe would like to thank the following for their support of this project: Aude Robert, intern at IMS Health; Grégory Coulthard, IT engineer at IMS Health; Alain Venot, Professor of Medical Informatics and Thierry Hamon, Professor of NLP, both at the University of Paris 13.


1 Herzog TN, Scheuren FJ, Winkler WE. Data quality and record linkage techniques. Springer-Verlag New York Inc.; 20072 bird S, Klein E, Loper E. Natural language processing with Python. 1st ed. O’Reilly Media, Inc, USA; 20093 Kukich K. Techniques for automatically correcting words in text. ACM Comput. Surv. 1992 Dec; 24(4):377–4394 Damerau FJ. A technique for computer detection and correction of spelling errors. Commun ACM, 1964 Mar;7(3):171–6

fIGURE 4: OVERALL GAIN OBTAINED THROUGH THE NLP PROJECT

Nb: Although the laboratory exam values (the numbers and units)were already well bridged, for 17 million records they were uselessas not related to a laboratory exam label.

Num

ber o

f brid

ged

(cod

ed) d

ata

item

s

60

50

40

30

20

10

0Laboratory exam

labels

before NLPMillion After NLP

Laboratoryexam values

After completion of the NLP project, HbA1C was available for 16.5% of patientswith diabetes, almost twice the previous rate of available information.

INSIGHTS | PROPENSITY SCORING

Alongside growing demands for real-world evidence (RWE) is the imperativeto ensure its reliable and confident interpretation. Propensity scoring has a keyrole to play in ensuring stakeholder trust in RWE insights but requires carefulconsideration of its relevance and application to preserve its value.


Vernon Schabert, PHD is Senior Principal RWE Solutions & HEOR, IMS [email protected]

Saeed Noibi, MPH is Consultant RWE Solutions & HEOR, IMS [email protected]

The Authors

Practical considerations for appropriate useThe need for real-world evidence (RWE) by healthcare payers and providers todemonstrate the effectiveness of medical interventions has been well established.However, concerns over the reliability of inferences from such evidence have been thesubject of ongoing methodological advances in research study design and analytics.1

Unlike randomized controlled trials (RCTs) in which the ‘ideal’ experimental environment is created, RWE relies on datagenerated from prevailing standards of care without any intended intervention. As much as RWE addresses questionsof intervention effectiveness in community settings, which is not the traditional focus of clinical trials, a real concern stilllies in the reliability of any ensuing association between healthcare interventions and outcomes.

PROPENSITY SCORING METHODRosenbaum and Rubin’s seminal paper in 1983 introduced the medical research community to propensity scoringmethod (PSM).2 Initially developed as a method to match patients on different exposures in order to minimizesystematic selection of patients for treatment (selection bias), PSM is a probabilistic model that collapses all measuredcovariates to generate one continuous variable that is predictive of the exposure of interest, eg, treatment. The statistical models used to develop propensity scores were not new to biostatisticians or outcome researchers, butthe use of the output from one model to control the inputs of a separate outcome model was less familiar in outcomesresearch than it was in other social sciences.

by the early 2000s, use of PSM had become established practice in outcomes research. Since then, perhaps due to thepopularity and varied applications of the methodology as well as the passage of time, it has attained both a mythicalstatus and a carelessness of application that undermines its value. The reliability of PSM over other methods ofcovariate adjustment depends heavily on its appropriate application and an understanding of its suitability given thestudy scenario (Figure 1).

PROPENSITY SCORING | INSIGHTS

Demystifying the propensity scoringmethod in real-world evidence generation



fIGURE 1: APPLICATIONS Of PROPENSITY SCORING METHOD

Dependent on robustmatching patient numbers

Dependent on: 1. Sufficient patient numbers 2. Presence of outliers

Potential to identifytreatment effect modifiers

Applied where it potentiallyconfers advantage toordinary modeling

Weighting by inverse ofpropensity scores to create a quasi-sample

Pre-agreed variables in the real-world data source:1. Predictive of intervention in the prevailing

standard of care2. Proxies for unmeasured covariates

Validation of selected variables through externalinformation eg, expert opinion and sensitivityanalysis

Decision to proceed withdesign informed by soundmethodological rationale

Careful model fitting

STUDy DESIGN

ANALyTICS

PROPENSITySCORING

METHOD (PSM)

Matching onpropensity scores

Restricting patients byexcluding outliers in the

propensity

Stratification bypropensity score

Multivariate analysis ofpropensity scores

Weighting

I

I I


Selecting the right covariatesPSM lends itself, at least intuitively, to interpretation of the way in which selection bias occurs and therefore how thebenefits of randomization may be approximated in an observational study. However, this impression needs to becarefully considered in view of the fact that, from the outset, PSM is underpinned by an assumption that relevantcovariates to treatment selection are at least correlated with variables that are observable and have been included inthe propensity model.

PSM is a method for creating a multi-dimensional proxy variable for bias that may influence treatment effect estimates.It is therefore imperative that covariates included in the propensity model are those that are most predictive of theexposure treatment and the outcome.3,4 For example, covariates that are known, a priori, not to relate to the outcomeshould be removed from the propensity model from the outset. In establishing the degree of representation of this finallist of variables to be included, external information should be used to validate the selection of covariates.5,6 This mayinclude expert opinions and sensitivity analyses.7

Ensuring good model-fittingPropensity scores are estimated using logistic regression. As with all models, careful model-fitting is essential to assurethe reliability of the resultant propensity score estimates. This includes attention to co-linearity among the covariates,examination of residuals to confirm whether effects are linear or non-linear, and consideration of estimates with largestandard errors. These are remedial steps for an attentive statistician but, unfortunately, the ease with which statisticalmodels may be fit in modern software packages unintentionally discourages these very steps. Insisting on carefulmodel diagnostics is critical in building a propensity model, as a poor-fitting model can actually obscure treatmentdifferences rather than clarify them.

Sometimes, propensity models do not fit well. This may be true when selection bias exists but is not measured directlyor indirectly by observed variables. It may also be true when selection bias does not exist. It is rarely possible todetermine which of these cases is true when a propensity model explains little variation in treatment selection.However, a propensity score should not be retained simply because stakeholders expect to see one; neither shouldpredictors that fail to explain variance in treatment selection be kept in a propensity model. Insistence on carefulmodel-fitting gives researchers greater confidence in rejecting a propensity model when it does not add value to theanalysis.8

Understanding the implications ofscore distributionA very important factor in deciding thesuitability of applying PSM in studydesign or analysis is the ability todecipher the messages conveyed by thedistribution of the propensity scores.Graphical plots of propensity scores ofcomparison groups help to show thelevel of overlap based on the baselinecovariates (Figure 2).

fIGURE 2: GRAPHICAL PLOTTING Of PROPENSITY SCORESDEMONSTRATES OVERLAP BETWEEN DIffERENT TREATMENT GROUPS

Minimal overlap of propensities score distribution in different treatment groups may bedue to extensive selection of treatment, or suggest critical variables are not used toestimate propensity scores.

Stud

y Po

pula

tion

(1 X

103 )

Hypothetical propensity score distribution

35

30

25

20

15

10

5

00 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Treatment A Treatment b

INSIGHTS | PROPENSITY SCORING


A propensity score graph with minimal overlap of the treatment groups suggests extensive selection of treatment; sucha strong selection bias may limit the ability to control for this bias in the ultimate outcomes models. However, suchdistribution may also suggest that the PSM does not have the most critical variables required.

While nearest-neighbor matching (pairing or matching observations with very similar propensity scores) remains apopular application of propensity scores,9 other applications may also control for selection bias. Regression adjustmentby using the propensity score in the outcome model has the advantage of allowing all study observations to be usedrather than discarding some without a clear nearest-neighbor match. In addition, propensity score matching withincaliper would stratify analyses across the range of observed propensity scores which can be useful when a clearhypothesis exists on how treatment propensity may influence outcomes across the study population. Matchingtechniques may be helpful, but they also risk lending a sense of overconfidence in the degree of control in makingstudy populations look subjectively more like those from a clinical trial. Matching techniques also discard substantialcovariance in the original population when strong selection biases are detected. As with model-fitting, the applicationof control techniques should be driven as much as possible by a priori hypotheses and careful consideration of thelikely influence of selection bias on outcomes.

CONCLUSION While PSM is a simple and intuitive approach to adjusting for confounding, an unobservant researcher may err in theapplication of the method or indeed interpretation of the evidence. The reliability of PSM is dependent on theappropriate selection of covariates and specification for the estimation of propensity score by regression model.Furthermore, the way in which the PSM is used in a regression model of the outcome should be selected with carefulattention to the level of precision it implies. PSM is a critical tool for strengthening stakeholder trust in analyses of RWEbut attentive and responsible use is critical to maintain the perceived value of this technique and of the data to which itis applied. •

1 brunelli SM, Rassen JA. Emerging analytical techniques for comparative effectiveness research. Am J Kidney Dis. 2013 Jan; 61(1):13-7 2 Rosenbaum PR, Rubin Db. The central role of the propensity score in observational studies for causal effects. biometrika. 1983; 70(1):41–553 Patrick AR, Schneeweiss S, brookhart MA,Glynn RJ,Rothman KJ, Stürmer T. The implications of propensity score variable selection strategies in pharmacoepdemiology: An

empirical solution. Pharmacoepidemiol Drug Saf. 2011 Jun; 20(6):551-94 brooks JM, Ohsfeldt RL. Squeezing the balloon: Propensity scores and unmeasured covariate balance. Health Serv Res. 2012 Dec 6. doi: 10.1111/1475-6773.120205 Seeger JD, Kurth T, Walker AM. Use of propensity score technique to account for exposure-related covariates. An example and lesson. Medical Care. 2007 Oct; 45

(10 Suppl 2):S143-86 Westreich D, Cole SR,Funk MJ, brookhart MA, Til Stürmer. The role of the c-statistics for propensity score models. Pharmacoepidemiol Drug Saf. 2011 March; 20(3):317-320 7 Li L, Shen C, Wu, Li X. Propensity score-based sensitivity analysis method for uncontrolled confounding. Am J Epidemiol 2011 Aug 1; 174(3):345-538 Faries D, Peng X, Pawaskar M, Price K, Stamey JD, Seaman JW Jr. Evaluating the impact of unmeasured confounding with internal validation data: an example cost

evaluation in type 2 diabetes. Value Health 2013 Mar-Apr; 16(2):259-669 Austin PC. A critical appraisal of propensity-score matching in the medical literature between 1996 and 2003. Stat Med. 2008 May 30; 27(12):2037-49

PROPENSITY SCORING | INSIGHTS

PSM is a critical tool for strengthening stakeholder trust in analyses of RWE but attentive and responsible use is critical to maintain the perceived value of this technique.

INSIGHTS | ObSERVATIONAL STUDY DESIGNS

As new realities in the healthcare landscape redefine the informationrequired for market access, the use of observational research isincreasingly relevant. Understanding observational study designs, their respective benefits and biases is essential to ensuring robust andrigorous data collection and real-world analyses.

Núria Lara Surinach, MD, MSC is Principal RWE Solutions & HEOR, IMS [email protected]

Charles Makin, MS, MBA, MM, BS is Principal RWE Solutions & HEOR, IMS [email protected]

The authors


Ensuring credible and efficient evidence generationRandomized controlled trials (RCTs) have traditionally remained the gold standard forsafety and efficacy information, thus having the greatest impact on market access.However, recent developments have accelerated the demand for greatertransparency around the real-world performance of pharmaceutical products in anon-controlled environment. The Affordable Care Act of 2010 (ACA) in the US, the implementation of the AMNOG law in Germany, and the movetowards value-based pricing in the UK, have all indicated new models for payment and delivery of care, necessitatingthe generation and synthesis of better evidence on effectiveness and comparative efficiency.

This new direction has seen most pharmaceutical manufacturers rethinking their go-to-market strategy, with economicand patient-reported outcomes assuming greater significance in addition to clinical endpoints. At the same time, thereis an increased push towards personalized medicine and targeting patient subpopulations that would benefit mostfrom a drug (rather than a one-size-fits-all approach). With a stronger focus on external validity and real-world usage,this research – variously referred to as real-world evidence (RWE), health technology assessment (HTA) or observationalresearch – presents its own set of challenges that are distinct from traditional clinical research.

In order to better collect and interpret increasingly important observational data as the basis for efficient decision-making, it is imperative to understand the various ways it may be collected in a scientifically rigorous manner. Some ofthe more commonly-used study designs, their associated selection biases, and approaches to dealing with that bias, arediscussed below.

CASE-CONTROL STUDIESA case-control study is a type of observational analyticepidemiological investigation where subjects are selectedon the basis of whether they do (cases) or do not(controls) have a particular outcome of interest. Thegroups are then compared on the proportion having ahistory of an exposure (eg, risk factor, preventivetreatment, etc) or a characteristic of interest. As shown inTable 1, this type of study has the benefit of enablingrelatively fast and inexpensive investigation of multipleexposures. However, it also has a number ofdisadvantages, including a tendency towards selection,observer and recall bias.

Potential for selection bias in case-control studiesCase-control studies have a control group which serves torepresent the reference population, where cases comefrom. According to Schlesselman, this control group “isintended to provide an estimate of the exposure rate thatwould be expected to occur in the cases if there were noassociation between the study disease and exposure”.1

ObSERVATIONAL STUDY DESIGNS | INSIGHTS

Study designs in observational research



TABLE 1: ADVANTAGES AND DISADVANTAGES Of CASE-CONTROLSTUDIES

Advantages

Disadvantages

• Can investigate multiple exposures• Can be relatively quick and cheap• Good for rare outcomes

• Prone to selection, observer andrecall bias

• Time sequence of events difficult toascertain

• Not good for rate exposures• Limited to one outcome• Cannot estimate incidence

INSIGHTS | ObSERVATIONAL STUDY DESIGNS

The ideal case control study with no selection bias is one where:1. There is a clearly defined population (reference population)2. All cases in that population are included in the study3. Controls are a random sample of that population

The potential for selection bias in case-control studies is particularly great in the common situation where cases andcontrols are drawn exclusively from hospitals or clinics. The main factors that drive the use of hospital rather thancommunity controls are:

• Logistical reasons (it is easier and cheaper)

• To reduce recall bias

• To define population as “hospital users”

The problem with using hospital controls is that individuals in this setting typically do not represent the population ofpotential hospital users, but rather those who are sick. As such, they tend to be poorer, heavier smokers and drinkers,and living in worse conditions than the population of potential hospital users. Thus, should one of the factors that isover-represented in hospital controls be the exposure of interest, selection bias is introduced.

Dealing with selection bias in case-control studiesAs already noted, selection bias is likely to be less of a problem in population-based case-control studies where thecases are sampled from all incident cases in a defined population – the controls being sampled at random from thesame population. The defined population is often, but not necessarily, a geographic area and period of time.

If cases are identified through a hospital or clinic, the use of neighborhood controls may be preferable to controlsdrawn from other patient groups in the hospital or clinic. However, this would not be the case if the probability of beinghospitalized with the disease of interest was related to the exposure of interest. In this situation, the most appropriatesource of controls may be individuals who are hospitalized with other diagnoses, where the probability of beingadmitted to hospital was similarly related to exposure.

Rather than trying to identify the perfect control group, some researchers choose to select controls from more than onesource. The logic of this strategy is the unlikelihood that the effects of selection bias resulting from the use of twoseparate control groups would be identical. On this basis, it is reasonable to feel relatively confident that a majorselection bias has been avoided if the estimated relative risks are the same using the two different groups. However, the conclusion reached is less clear if the estimates differ.

COHORT STUDIESCohort studies begin with defining a group of people according to their exposure (eg, treatment, risk factor,intervention, etc) status. These groups are then followed up over time to see who develops the outcome of interest. As shown in Table 2, while these studies can be time consuming and expensive, if appropriately designed they canallow for the examination of multiple exposures.

Potential for selection bias in cohort studiesSelection bias tends to be less of a problem in cohort studies. This is mainly because it is usually easier to see when biasmay occur, which is typically when the exposed and unexposed groups are drawn from different populations.

The simplest form of selection bias in a cohort study is when completeness of follow-up or case ascertainment differsbetween exposure categories. In general, this problem is dealt with pragmatically by treating study findings withextreme caution if follow-up in any group is below some arbitrary level (eg, 80%), when differential case ascertainmentcould seriously bias results.

Another form of selection bias in cohort studies occurs when comparisons are made between disease rates in the studycohort and disease rates in some external standard population. Selection bias is introduced if membership of theexposed cohort is partly dependent upon health – which in itself may be related to the presence or absence of thedisease being studied. This type of health-related selection is frequently encountered in occupational studies where thepeople in the study (exposed) cohort (eg, miners) are, by definition, relatively healthy because they are in employment.


ObSERVATIONAL STUDY DESIGNS | INSIGHTS

Thus, the mortality or morbidity rates in the occupationalcohort are (initially at least) lower than those seen in thepopulation as a whole which includes those people too illto work. This bias is known as the healthy worker effect.

Two approaches may be used to address the healthyworker effect: either make all comparisons internal to theemployed study population, or make comparisons with anexternal standard which is composed of employed people.

CASE CROSSOVER DESIGN In the case crossover design, each case acts as its owncontrol, being the control period that was previous to theexposure of interest.2 As this type of study is selfcontrolled, confounding factors that are stable over time,such as genetics, are removed. This design is appropriatefor assessing acute effects of transient exposures.

As in a case-control study, the first step is to identify allcases (those with the outcome of interest) and assess theprevalence of exposure before the outcome occurred.With each case serving as its own control, this creates aseparate observation period containing the same variablesexcept for the exposure of interest. It is important that thecontrol time period is the same length as the case period.

Clearly, the main advantages of this study design are the fact that there is no need to select controls, and the ability to assess short-term reversible effects. However, there aresome limitations which can reduce its efficiency. These are mainly due to the fact that only cases with discrepantexposure history can contribute information to the analysis. It is also important to take into account that, while thedesign avoids confounding by factors that are stable over time, it can still be confounded by factors that vary over time.

COMBINING PROSPECTIVE OBSERVATIONAL DATA COLLECTION WITH RETROSPECTIVE DATA Rather than bucketing data-collection methods as prospective or retrospective, an efficient way of addressing researchand business problems is to combine both data streams when possible. Data collected prospectively are often the mostcomprehensive, being collected with a specific research question in mind. However, there is an attendant time andresource investment. by supplementing prospective data with the correct retrospective data, it is possible to reducecollection timelines and cost without compromising the integrity of the research.

IMS has often combined retrospective medical record abstraction with patient surveys. While medical records provideaccurate and up-to-date clinical information on the patient, the surveys provide insights into patient-reportedoutcomes (PROs) such as health-related quality of life (HRQoL), activities of daily life (ADL), preferences, and the indirectburden of a health condition. More recently, IMS has also combined primary data collection with information from itsdatabases, linking PROs with, for example, data on adherence and persistence from prescription claims databases. Withresearch demonstrating that self-reported medication adherence generally overstates actual adherence, obtaining ameasure of refill adherence from prescription records provides a more accurate means to assess refill rates andunderstand the impact of adherence interventions.

As the importance and frequency of observational research increase, so will scrutiny on the appropriateness of themethods chosen. Using the most suitable study design for a research question and accounting for its inherent biaseswill not only make the study more credible with the intended audience, it will also address the research and businessneed more efficiently. •


TABLE 2: ADVANTAGES AND DISADVANTAGES Of COHORT STUDIES

Advantages

Disadvantages

• Incidence can be measured• Time sequence ascertained (exposure

can be measured before disease onset)• Rare exposures can be investigated if

cohort groups appropriately selected• Multiple outcomes can be studied

• Time consuming and expensive• Losses to follow-up can cause

serious bias• Ascertainment of outcome may be

influenced by knowledge ofexposure status

• Classification of individuals(exposure or outcome status) can beaffected by changes in diagnosticprocedures, natural changes overtime, etc

• Not good for rare outcomes

1 Schlesselman JJ, 1982. Case-control studies: Design, conduct, analysis. New York: Oxford University Press2 Maclure M. The case-crossover design: A method for studying transient effects on the risk of acute events. Am J Epidemiol. 1991; 133:144-53


INSIGHTS | HTA IN LATIN AMERICA

Growing HTA requirements in Latin America (LATAM) are increasing the need for real-world data to support the inclusion of drugs and devices in national andlocal formularies. Along with new challenges for HEOR are opportunities tofacilitate use of this evidence in the fast evolving markets of the region.

Renée JG Arnold, PHARMD, RPH Principal RWE Solutions & HEOR, IMS [email protected]

The author



The path to value-based healthcareIn common with most developed and developing countries around the world, thehealthcare systems of Latin America (LATAM) face the challenge of balancingescalating costs with the need to achieve quality improvements. At the same time, an increasingly chronic disease burden and growing demand for new, often moreexpensive, medicines is adding a further layer of complexity. Reflecting these trends, the evolution of the pharmaceutical market has been characterized in recent years by morepronounced use of generics, often first-line, as a tool for cost containment, the establishment of national and local drugformularies, and greater employment of price references for trading internationally.

Throughout the region, a focus on approaches that enable more efficient, value-driven healthcare, while broadeningaccess and encouraging innovation, has seen more countries turning to the use of health technology assessment (HTA)as part of the strong push for healthcare reform. Supporting this move is the recent launch of RELACSIS (Latin Americanand Caribbean Network for the Strengthening of Health Information Systems) by the Pan American Health Organization(PAHO), which is actively promoting HTA in the region. Among the key stated goals of this leading initiative are theproposal of standards for producing higher quality, more reliable and timely information, the generation and sharing ofpractices, lessons and knowledge, promotion of the monitoring and evaluation of national information systems, andcooperation between countries.1

HEALTH TECHNOLOGY ASSESSMENTHTA has been defined as “a form of policy research that examines short- and long-term consequences of the applicationof a healthcare technology. Properties assessed include evidence of safety and efficacy, patient-reported outcomes,real-world effectiveness, cost and cost-effectiveness as well as social, legal, ethical and political impacts”.2 The adoptionof HTA principles, including incorporation of economic information into these evaluations, differs significantly bycountry.

Various factors influence the use of HTA, both in LATAM and globally. Surprisingly, perhaps, market size is notnecessarily associated with greater use of HTA and health economic (HE) information. Three of the largest globaleconomies in terms of GDP – the US, China and Japan – do not possess the most sophisticated HTA processes. Nor docountries with the highest healthcare costs per capita (eg, Switzerland, Norway, US). However, greater use of HEinformation does appear to correlate with the presence of guidelines for submission, HTA requirements and centralizeddecision making. Factors which appear to have a negative association are complexity of the healthcare system (bothhigh and low complexity may reduce use of HE), poor data accessibility and limited availability of trained individuals toconduct and interpret studies.

In most emerging markets, mandatory and recommended HE evidence requirements are increasing (Figure 1). InLATAM, a growing number of countries have been moving towards some form of HTA process or activelyinstitutionalizing HTA. Argentina, brazil, Chile and Mexico now have formal HTA agencies in place which are part ofINAHTA (International Network of Agencies for Health Technology Assessment). Colombia, Costa Rica, Cuba, Paraguay,Peru and Uruguay are also following in a similar direction and are among a number of nations in the region that havejoined REDETSA – the Health Technology Assessment Network of the Americas, established in 2011 through PAHO topromote and strengthen HTA processes across the Americas.

HTA IN LATIN AMERICA | INSIGHTS

Evolution of HTA and pharmacoeconomicanalyses in Latin America



IMS HEOR & REAL-WORLD EVIDENCE SOLUTIONSPage 1 IMS HEALTH ECONOMICS AND OUTCOMES RESEARCH

MARKERS OF PROGRESSAn indication of the progress being made towards HTA in LATAM can be seen in the growing use ofpharmacoeconomic evaluations as a decision making toolin a number of markets. In Mexico, for example, they aremandatory for listing in the national formulary.

In brazil, they are now part of the pricing process forinnovative patented drugs, reflecting the country’s muchstricter approach to reimbursement following the recentcreation of its new HTA agency, CONITEC (NationalCommission for Incorporation of Technologies in theUnified Healthcare System).

In Argentina, economic studies are recommended for allactive ingredients included in the national formulary andhospital listings. And in Colombia, after many years in themaking, the establishment of a national HTA agency (IETS)is finally now coming to fruition.

Within each of these countries, different payers have different perspectives and needs: national formularies are typicallyfocused on cost-effectiveness analysis and adaptations; those at the local level are principally concerned with theimmediate financial implications of treatment as demonstrated through budget impact analyses.

HTA DEVELOPMENTIn the drive to achieve more efficient use of healthcare resources, governments typically seek tools to help themdetermine the value of innovations and reimburse accordingly, often through the use of national, independent HTAagencies, eg, CONITEC and NICE (National Institute for Health and Care Excellence) in England and Wales. As they shiftfrom a position of covering drugs as and when approved, to questioning the value of innovative new technologies andultimately managing treatment algorithms and patient access to therapies, they are increasingly reliant on real-worlddata. Commensurate with requirements and emerging market value growth, HTA evidence in LATAM has beenconsistently increasing in recent years (Figure 2).

CHALLENGES OF HTA IN DEVELOPING COUNTRIESMany challenges exist in fulfilling the requirements of HTA processes in LATAM, not least: the lack of reliable data andinformation sources; the scarcity of theory studies to conduct decision making; the shortage of skills and conceptualknowledge to appraise the results of analyses; poor dissemination of findings; and the high degree of institutionalfragmentation. However, by their very nature, these hurdles also present considerable opportunities to optimize theuse of HE evidence in the region. They are by no means unique to LATAM and as the HTA agenda moves forward,examples of innovative practices and solutions employed in other selected markets can offer useful direction forovercoming the challenges of developing value evidence throughout the product lifecycle while supporting efforts at the country level to reach evidence-based decisions in accordance with the local situation.



The ability to communicate research findings to relevant stakeholders and thebroader healthcare community is essential to raising awareness and strengtheningthe local evidence base for decision making.

fIGURE 1: MANDATORY AND RECOMMENDED HEALTH ECONOMICEVIDENCE REQUIREMENTS ARE INCREASING IN EMERGING MARKETS

YEAR 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Sources: www.ispor.org; IMS Consulting analysisPE: Pharmacoeconomic

PEguidelines

PErecommends

Submissionguidelines

ThailandPoland

Israel

Cuba

Taiwan

Mexico brazil

Colombia

S. Africa

Russia

China

ACCESSPOINT • VOLUME 3 ISSUE 5 PAGE 47OUTCOMES - Issue 1 Page 1

OVERCOMING THE CHALLENGES: EVIDENCE GENERATION

1. Developing credible data: Saudi Arabia and US

Approaches to overcoming the lack of robust, reliable data have seen the use of partnership collaborations by manyorganizations to generate an evidence base. These include several recent examples in the Middle East and US:

• GE Healthcare partnership with Saudi Ministry of Health on Healthymagination

Launched in 2009 with the goal of providing greater access to healthcare services in Saudi Arabia, this initiativeincluded a consumer campaign of health and wellness research to raise awareness of healthy living and earlydiagnosis.

• National Headache Foundation/Ortho McNeil Neurologics AMPP study

The American Migraine Prevalence and Prevention (AMPP) Study – the largest ever analysis of headache sufferers –is based on data compiled from 2004 through 2009 examining nearly 163,000 Americans ages 12 and older,selected to be representative of the US population. It has yielded extensive data on symptoms and treatmentpatterns.

• AstraZeneca/HealthCore real-world evidence (RWE) data collaboration (US)

Announced in 2011, this collaborative agreement between AstraZeneca and HealthCore has the goal ofconducting real-world studies designed to determine how to most effectively and economically treat disease, witha special emphasis on chronic illnesses.




Development and implementation of pharmacoeconomic (PE) guidelines in Latin America

Argentina Colombia Guatemala Uruguay Venezuela

Mexico

Chile

Brazil

Clinical and cost-e�ectiveness – through the application of PE guidelines being considered to be, and could well be, incorporated within the existing set of policy instruments. For example, Colombia established HTA Institute (IETS) in 2011.

A set of PE guidelines has been developed and a law established requiring a PE dossier before inclusion of a technology in the national formulary (NF). After inclusion in the NF, each healthcare institution can then decide whether or not to purchase based on its cost, budget availability, previous experience with the technology at the institution concerned, and priority of disease.

Clinical guidelines have been established in several di�erent pathologies based on a prioritization process.

The Latin American country with the most experience in implementing PE guidelines. Implementation is the responsibility of ANVISA and the Ministry of Health (MoH). PE is applied in pricing decisions for new drugs. CONITEC, the HTA assessment arm of the MoH ,requires manufacturers to provide cost-e�ectiveness and HTA evaluations in support of pricing.

fIGURE 2: COMMENSURATE WITH REQUIREMENTS AND EMERGING MARKETVALUE GROWTH HTA EVIDENCE HAS INCREASED IN LATIN AMERICA

Source: Augustovski f, Melendez G, Lemgruber A, Drummond M. Implementing pharmacoeconomic guidelines in Latin America: Lessons learned. Value Health 2011; 14 (Suppl. 1): S3-7



• AstraZeneca/IMS Health collaboration to expand RWE (Europe)

Announced in 2012, this collaborative agreement between AstraZeneca and IMS Health has the goal of advancingthe use of RWE, based on observational and retrospective studies throughout Europe, to inform the delivery ofeffective and cost-efficient healthcare.

2. Improving conceptual knowledge: Thailand

In the absence of the established capabilities and means for determining optimal healthcare resource allocation,external expertise can play a key role in helping healthcare systems create the evidence they need to make locally-relevant decisions.

An example can be seen in the case of cervical cancer vaccine coverage in Thailand. Under pressure from themultinational drug industry, international agencies, and patient and professional groups, the Thai FDA licensed twoHPV vaccines. However, when it commissioned an independent, expert body, HITAP (Health Intervention andTechnology Assessment Program), to “generate reliable and relevant information” using local Thai data, it was able toascertain that the vaccine’s ICER (incremental cost-effectiveness ratio) was three times Thailand’s GDP per capita.Although this metric (3x GDP/capita) may be considered cost-effective in emerging markets, in this case, based on thistransparent, consultative scientific approach, the vaccine was not included in the universal coverage program.

OVERCOMING THE CHALLENGES: LESSONS LEARNED

1. Broadening dissemination of findings

The ability to communicate research findings to relevant stakeholders and the broader healthcare community isessential to raising awareness and strengthening the local evidence base for decision making. Experience has shownthat engaging stakeholders earlier in the product lifecycle is critical, with opportunities to create pull-through usingnon-branded modeling tools.

Examples include, in early development, the use of exploratory analyses to determine the value of a hypothetical newintervention or change in practice; and during the pre-launch phase, raising disease awareness through the use of, eg, disease state models, adherence modeling and demonstration of economic burden. Analyses of patient flow in adisease can help to identify areas of opportunity and unmet need in a local treatment path; the use of longitudinaldatabases can serve to inform treatment patterns and resource-use issues in clinical practice; and patient-reportedoutcomes (PROs) can be used to define influences of a disease on patient quality of life, thereby giving a baseline tomeasure drug effects in orphan or other illnesses.

The capacity to conduct rigorous outcomes research studies has been dramatically expanded in recent years with thedevelopment of RWE platforms for data exchange, such as IMS’ PharMetrics Plus™. by allowing the seamless integrationof multiple patient-level data assets, these can enable a complete and holistic picture of the patient journey acrosssettings of care.

2. Building institutional cohesion

Efforts to improve decision making and healthcare at the local level have encompassed various partnerships andcollaborative efforts in brazil, Germany, Spain, UK and US. Examples include the establishment of Primary Care Trustalliances for sub-regional access decision making in the UK, industry partnerships with private insurers to demonstratecost savings from outpatient drug coverage in brazil, and the development of interactive hospital/clinic perspectivetools to quantify treatment-related costs associated with current and future practice in the US.

3. Raising the quality of theory studies

In markets where cost per QALY is not a standard measure, analyses can be adapted to inform current decision making(Figure 3). For example, in emerging markets, such as China, the metric used to identify cost-effectiveness is <3x GDP.

As the move towards HTA continues to gather momentum in the markets of LATAM, companies looking to participatein their evolution towards evidence-based decision making can benefit from the lessons learned in adapting globalmodels to other emerging markets.



These provide a number of important pointers in the steps to developing robust and relevant real-world data in the region:

1. Create as sophisticated an analysis as country level, budget and time frame will allow, taking into consideration:

• Level: Sophistication of country-specific pharmacoeconomic/HTA guideline

• Budget: Consider using local labor

• Time frame: Prepare key opinion leader (KOL) expectations in advance

2. Collect local literature (both in English and translated from local language if not a native speaker) about practicepatterns in country of interest

3. Retrieve and translate international disease treatment guidelines, if available

4. Use local cost sources (depending on model perspective: country, region, large hospital, local KOLs)

5. For budgetary impact models, use local market access (market share) figures for baseline

CONCLUSIONSIn summary, health expenditure, particularly in the public sector, is increasing and governments have the challenge toimprove health outcomes with a limited budget. As such, the requirements for HTA are increasing and evolving inLATAM in terms of their sophistication and RWE components, to enable governments to meet this challenge usingobjective evidence. Indeed, LATAM countries are taking their cues from more seasoned HTA authorities such as NICEand PAHO is actively promoting HTA in the region. In several countries, cost-effectiveness/HE analysis is already anessential part of the access process; in others, it is increasing in importance. Outcomes research, using RWE from theregion/country, is also particularly important, given the lack of information regarding treatment paradigms, burden ofillness and unmet medical need. •

1 Strengthening Health Information Systems - RELACSIS. Accessed 21 April at http://new.paho.org 2 International Society for Pharmacoeconomics & Outcomes Research (ISPOR). Healthcare Cost, Quality and Outcomes: ISPOR book of Terms, Laurenceville NJ: 2003

fIGURE 3: ANALYSES CAN BE ADAPTED TO INfORM DECISION MAKING IN MARKETS WHERE COST/QALY IS NOT STANDARD

UK

S.KOREA

WHAT IS COST IMPACT? WHAT IS RELATIVE VALUE?

• Show budget impact for national payer

• Show budget impact for national payer

• Within trial analysis of healthcareresource use adapted in US

• CEA model developed for NICEsubmission in UK. Leveraged within trialanalysis of economic data. benchmarkICER 30K/QALY in UK

• CEA model adapted in S. Korea withKorean-specific parameters where possible.No official benchmark ICER in S. Korea;anecdotal range from 10M -30M KRW,depending on perceived urgency to treat

• No CEA model requirement in USUS

INSIGHTS | AMNOG PATIENT-RELEVANT ENDPOINTS

Two years after AMNOG brought new stipulations for drug assessment inGermany, pointers are emerging on the practical implications for demonstratingadditional benefit. Analysis of dossiers reviewed to date in two significanttherapeutic areas provides important insights into the hard patient endpointsthat are key to successful market access in this country.


Dirk Eheberg, MPH is Senior Consultant RWE Solutions & HEOR, IMS [email protected]

Doreen Bonduelle, FH is Director RWE Solutions & HEOR, IMS [email protected]

Stefan Plantör, PHD, MBA, MSC is Director RWE Solutions & HEOR, IMS [email protected]

The Authors

There remains uncertainty around the term ‘patient-relevance’ and the criteria that have to be met.

Why the right study endpoints are keySince the introduction of AMNOG (Arzneimittelmarktneuordnungsgesetz; Law on theReorganization of the Pharmaceutical Market) in January 2011, manufacturers inGermany have been required to submit a ‘benefit dossier’ when launching a newactive ingredient. Ensuring the right patient-relevant endpoints can be decisive forthe Federal Joint Committee (G-bA) resolution on the additional benefits conferred.Preparation for the AMNOG process (see Figure 1, page 55) should therefore start as early as possible. Incorporatingpatient-relevant endpoints into the study design while planning the clinical program is the most promising approach.However, this option is not always straightforward as international clinical studies must meet the requirements of notonly marketing authorization agencies but also a rapidly growing number of health technology assessment (HTA)organizations from different countries. The degree to which these HTA bodies overlap in their requirements, especiallywith regard to endpoint design, is relatively small,1 making selection of the right study design even more complex.

ENSURING PATIENT-RELEVANCE One of the most important arguments to bring forward within the AMNOG dossier is the patient relevance of theclinical study endpoints. The G-bA clearly states in §3 of Chapter 5 of the Code of Procedure (G-bA Verfahrensordnung)that patient-relevant endpoints are only those that can be classified into one of the following categories:

• Improvement in the state of health

• Shortening of duration of illness

• Extension of survival

• Reduction of side effects

• Improvement in quality of life (§3 par 1)

The Institute for Quality and Efficiency in Healthcare (IQWiG) defines patient-relevant endpoints in its paper onmethodology (IQWiG Methodenpapier 4.0) as any endpoint that directly relates to how a patient feels, is able tofunction or survives.

However, there remains uncertainty around the term ‘patient-relevance’ and the criteria that have to be met in order forIQWiG to accept an endpoint as patient-relevant. Perhaps the greatest public misconception is that therapeutically-relevant equals patient-relevant. The G-bA and IQWiG have demonstrated quite clearly that therapeutic relevanceneeds to be translated into patient relevance and that this translation is not given by concept. A therapeutically-relevant endpoint (eg, controlling blood pressure in patients with coronary heart disease), is not per se patient-relevant.In order to appreciate this difference, it is necessary to understand the concept of a surrogate parameter.

AMNOG PATIENT-RELEVANT ENDPOINTS | INSIGHTS

AMNOG: Additional benefit demandspatient relevance




SURROGATE PARAMETERS Surrogate parameters measure directly a parameter that is an indicator for a (patient-relevant) event which is eitherhard to track (eg, rare events) or is a long-term consequence. As such, surrogates indirectly measure patient-relevantendpoints and are commonly used as mediators between therapeutically-relevant endpoints and patient-relevantendpoints. However, surrogate parameters are only to be considered if they are validated for the indication and thesubstance in question. Furthermore, surrogate parameters will be ignored by the G-bA and IQWiG if the endpoint theyreplace can be derived from the clinical study reports.

A well-known illustration of this concept is in the area of overall survival/progression-free survival. IQWiG and G-bAconsider progression-free survival as a non-validated surrogate for overall survival. In its report on the validity ofsurrogate endpoints in oncology2, IQWiG states on the one hand that “a mere correlation between a surrogate andpatient-relevant endpoint is not sufficient for validation”, but on the other hand leaves it open in stating that “there isno universal measure nor common estimate nor threshold which is to be exceeded to gain validity for surrogates”. Forexample, the question of whether endpoints relating to tumor-progression are surrogates has been under discussion.One way to argue for their patient-relevance can be seen in the IQWiG report on allogeneic stem-cell transplantation inHodgkin’s lymphoma3. Here tumor progression is reported as a derivate of survival time, with the endpoints“progression-free survival” or – quoting IQWiG – “comparable endpoints” (eg, disease-free survival). Possible reasonscould be that patients are younger, in the early stage of disease or have longer life expectancy.

EVIDENCE TO DATEThe practical implications of guidance regarding patient-relevant endpoints can best be considered in the light ofevidence from AMNOG evaluations to date. A recent IMS study, drawing on the IMS AMNOG database, analyzed allsubmitted (as of 15 March 2013) benefit dossiers in the indications of infectious disease and oncology, for which at leastthe first written statement from IQWiG for complete dossiers, and from the G-bA for orphan drug dossiers, wereavailable. The analysis (shown in Table 1) demonstrates that apart from overall survival there is little overlap betweenthe manufacturer, IQWiG and G-bA assessment of endpoints.

Infectious diseaseDossiers in the indications HIV (rilpivirin) and chronic hepatitis C (telaprevir; boceprevir) included the endpoints ‘viralresponse’ and ‘sustained viral response’. IQWiG regarded both endpoints either as sufficient validated surrogates(rilpivirin, telaprevir) or as not validated surrogates (boceprevir). The G-bA accepted viral response as a sufficientvalidated endpoint and agreed with the pharmaceutical manufacturer that sustained viral response is a patient-relevant endpoint (without clarifying whether it is a validated surrogate endpoint or not a surrogate endpoint). Allother endpoints, such as viral failure, relapse rate, rapid response or immunological response were marked by IQWiG assurrogate parameters or as redundant to the (sustained) viral response and were consequently not considered by theG-bA for the benefit assessment.

OncologyIn oncology, the benefit assessment for new products follows the same pattern. Overall survival is the only widelyaccepted patient-relevant endpoint. In addition, endpoints regarding symptoms, pain (response and progression) ortime to tumor-specific event (eg, skeletal events for abirateronacetat) were considered patient-relevant by IQWiG andthe G-bA consecutively. Progression-free survival, tumor response, objective response and tumor progression werealways marked as not validated surrogates for overall survival by IQWiG and were not considered for the benefitassessment by the G-bA.


In the benefit assessment for new products in oncology, overall survival is the only widely accepted patient-relevant endpoint.



TABLE 1: ANALYSIS Of AMNOG DOSSIER SUBMISSIONS IN INfECTIOUS DISEASE AND ONCOLOGY TO 15 MARCH 2013


Substance Dossiertype Endpoint

Statement frompharmaceuticalmanufacturer

Written statement from IQWiG or G-BA G-BA decision

Infectious Diseases (HIV; Chronic hepatitis C)

Boceprevir Fulldossier

Overall survival No surrogate No surrogate No surrogate; patient-relevant

Sustained viral response(SVR) No surrogate Surrogate; not formally validated No surrogate; patient-relevant

Rilpivirin Fulldossier


Viral response (viral load) Patient-relevantsurrogate

Sufficient validated surrogate; not necessarilypatient-relevant

Formally validated surrogate; patient-relevant for the present indication

Viral failure (effectiveness) No surrogate Redundant; endpoint already considered byviral response Not considered

Viral failure (resistance) No surrogate Redundant; endpoint already considered byviral response Not considered

Rilpivirin triplecombination

Fulldossier

Overall survival No surrogate No IQWiG assessment: Incomplete dossier No surrogate; patient-relevant

Viral response (viral load) Patient-relevantsurrogate No IQWiG assessment: Incomplete dossier Formally validated surrogate; patient-

relevant for the present indicationViral failure (effectiveness) No surrogate No IQWiG assessment: Incomplete dossier Not considered

Immunological response:CD-4-cell count Surrogate No IQWiG assessment: Incomplete dossier Not patient-relevant

Telaprevir Fulldossier

Overall survival No surrogate No IQWiG assessment: Incomplete dossier No surrogate; patient-relevant

Sustained viral response(SVR)

Patient-relevantsurrogate

Sufficient validated surrogate; not necessarilypatient-relevant No surrogate; patient-relevant

Relapse rate No surrogateSurrogate; no detailed information onvalidation by PC; adequately taken intoaccount by SVR

---

Rapid virologic response No surrogate? Possibly surrogate; not validated; notnecessarily patient-relevant ---

Extended rapid virologicresponse No surrogate? Possibly surrogate; not validated; not

necessarily patient-relevant ---

Fatigue No surrogate No surrogate ---Oncology

Abirateronacetat Fulldossier

Overall survival No surrogate No Surrogate No surrogate; patient-relevantRadiographic progression-free survival Surrogate Surrogate; no sufficient explanation/validation Not considered

Prostate-specific antigenresponse Surrogate Surrogate; no sufficient explanation/validation Not considered

Time to PSA progression Surrogate Surrogate; no sufficient explanation/validation Not consideredTime to first skeletal event No surrogate No surrogate No surrogate; patient-relevantTime to pain progression No surrogate No surrogate No surrogate; patient-relevant

Axitinib Fulldossier

Overall survival No surrogate No surrogate Not completed yetSymptomatic No surrogate No surrogate Not completed yet

Progression-free survival No surrogate Surrogate; not valid; not patient-relevant formorbidity or life quality Not completed yet

Objective response rate No surrogate Surrogate; not valid; not patient-relevant formorbidity or life quality Not completed yet

Brentuximab Orphandrug

Overall survival No surrogate No surrogate Not completed yet, but written statementrepresents G-BA point of view (Orphan drug)

Event-free survival Surrogate Surrogate; no valid surrogate for patient-relevant endpoint ‘overall mortality’

Not completed yet, but written statementrepresents G-BA point of view (Orphan drug)

Progression-free survival Surrogate Surrogate; no valid surrogate for patient-relevant endpoint ‘overall mortality’


Objective response rate Surrogate Surrogate; unclear validity Not completed yet, but written statementrepresents G-BA point of view (Orphan drug)

Remission rate for B-symptomatic No surrogate No surrogate Not completed yet, but written statement

represents G-BA point of view (Orphan drug)

Proportion of patients withstem cell transplantationafter treatment

No surrogateNo surrogate (assessment uncertain, asendpoint was not included in the finalassessment by the pc)


Complete remission No surrogate No surrogate (partly accepted) Not completed yet, but written statementrepresents G-BA point of view (Orphan drug)



Substance Dossiertype Endpoint

Statement frompharmaceuticalmanufacturer

Written statement from IQWiG or G-BA G-BA decision

Oncology ctd

Cabazitaxel Fulldossier


Change in pain score No surrogate No surrogate No surrogate; patient-relevant

Pain response No surrogate No clear surrogate, but redundant Not considered

Pain progression No surrogate No clear surrogate, but redundant Not considered

Mean AUC for pain score No surrogate No clear surrogate, but redundant Not considered

Progression-free survival Surrogate Surrogate without details on validity Not considered

Tumor progression No surrogate Surrogate without details on validity Not considered

Tumor response rate No surrogate Surrogate without details on validity Not considered

PSA progression No surrogate Surrogate without details on validity Not considered

PSA response rate Surrogate Surrogate without details on validity Not considered

Crizotinib Fulldossier

Overall survival No surrogate No surrogate Not completed yet

Progression-free survival No surrogate Surrogate; not validated; not patient-relevant Not completed yet

Objective response rateand associated endpoints(TTR (time to tumorresponse); DR (duration ofresponse); DCR (diseasecontrol rate))

No surrogate Surrogate; not validated; not patient-relevant Not completed yet

Symptomatic No surrogate No surrogate Not completed yet

Time to impairment No surrogate No surrogate Not completed yet

Decitabin Orphandrug

Overall survival No surrogate No surrogate Not completed yet, but written statementrepresents G-BA point of view (Orphan drug)

Response-relatedendpoints No surrogate

Unclear wording by the G-BA; validity andpatient-relevance of CR+CRp (+duration)conclusively not assessable; CR importantprognostic factor


Event-free survival No surrogate Surrogate; not validated Not completed yet, but written statementrepresents G-BA point of view (Orphan drug)

Recurrence-free survival No surrogate Surrogate; not validated Not completed yet, but written statementrepresents G-BA point of view (Orphan drug)

Hospitalization No surrogate No surrogate; not necessarily patient-relevant Not completed yet, but written statementrepresents G-BA point of view (Orphan drug)

Transfusions No surrogate No surrogate; not necessarily patient-relevant Not completed yet, but written statementrepresents G-BA point of view (Orphan drug)

Eribulin Fulldossier Overall survival No surrogate No surrogate No surrogate; patient-relevant

Ipilimumab Fulldossier


Complications No surrogate No surrogate, but wrong category (belongs toside effects) Considered among side-effects

Pixantron Fulldossier

Overall survival No surrogate No IQWiG assessment: Incomplete dossier No G-BA assessment: Incomplete dossier

Complete remission No surrogate No IQWiG assessment: Incomplete dossier No G-BA assessment: Incomplete dossier

Progression-free survival No IQWiG assessment: Incomplete dossier No G-BA assessment: Incomplete dossier

Vandetanib Fulldossier

Overall survival No surrogate No IQWiG assessment: Incomplete dossier Not completed yet

Biochemical response Surrogate No IQWiG assessment: Incomplete dossier Not completed yet

Progression-free survival No surrogate No IQWiG assessment: Incomplete dossier Not completed yet

Objective response rate No surrogate No IQWiG assessment: Incomplete dossier Not completed yet

Time to pain progression No surrogate No IQWiG assessment: Incomplete dossier Not completed yet

Vemurafenib Fulldossier

Overall survival No Surrogate No surrogate No surrogate; patient-relevant

Progression-free survival No surrogate Surrogate; not validated Not considered

Tumor response No surrogate Surrogate; not validated Not considered

Change in pain score No Surrogate No surrogate No surrogate; patient-relevant

TABLE 1: ANALYSIS Of AMNOG DOSSIER SUBMISSIONS IN INfECTIOUS DISEASE AND ONCOLOGY TO 15 MARCH 2013 continued

Source: IMS Health AMNOG database



1 Neumann PJ, Drummond MF, Jonsson b, Luce bR, Schwartz JS, Siebert U, Sullivan SD. Are key principles for improved health technology assessment supported and usedby health technology assessment organizations? International Journal of Technology Assessment in Health Care, 2010; 26(1): 71-78

2 IQWiG. Aussagekraft von surrogatendpunkten in der onkologie [Validity of surrogate endpoints in oncology]. IQWiG Rapid Reports – Commission No. A10-105, 20113 IQWiG Allogene stammzelltransplantation mit nicht verwandtem spender bei der indikation hodgkin-lymphom [Unrelated donor allogeneic stem cell transplantation for

Hodgkin's lymphoma] Report N05-03F, 2010

As the validation of a surrogate endpoint is a very technical process and IQWiG sets extremely high standards forquality, IQWiG and G-bA rarely differ. The analysis shows that, for infectious diseases, the G-bA was more likely tooverthrow the IQWiG assessment, which stated in most cases that the (sustained) viral response either was a notvalidated or sufficient validated endpoint, but not necessarily a patient-relevant surrogate. No such difference ininterpretation of the available data is discernible in the benefit assessments for oncology.

KEY LEARNINGSSeveral important messages emerge from these findings: pharmaceutical manufacturers should assess the patientrelevance of endpoints in clinical studies in light of the HTA requirements; preference should be given to hardendpoints instead of surrogates; and when dealing with surrogate endpoints, special consideration should be given tothe following dimensions:

• Accordance with an accepted methodology

• Validation in the target indication in a population with comparable severity of the disease

• Verification of robustness and the basis for generalization concerning correlation of surrogate andpatient-relevant outcome

Going forward, companies should continue to look for consistency in the decisions of the G-bA and reflect thesedecisions in their clinical study design as well as in the presentation of these endpoints within the benefit dossiers. •

Institute for Qualityand Efficiency in

Health Care (IQWiG)

Commission possible Report

Dossier

No additionalbenefit

Reference price not possible Agreement

Retroactive Valid until the endof the process

Decision

Hearing Additionalbenefit

Noagreement

Notaccepted

BenefitAssessment

Federal JointCommitee

(G-BA)

BenefitAssessment

(internet publication)

Manufacturer

MarketLaunch

Market Launch

FRP = Fixed reference price

3 months 6 months 12 months 15 months

Manufacturer’sprice (set freely)

FRP Referenceprice

Discounted‘net’price

Discounted‘net’price

Institute for Qualityand Efficiency in

Health Care (IQWiG)

Cost/benefitassessment

Arbitration panel

Decision(eg, based on

international prices)

Manufacturer Head associationof the

SHI scheme(GKV)

Rebate Negotiations

Federal JointCommitee

(G-BA)

BenefitAssessment(Decision)

fIGURE 1: THE AMNOG PROCESS IN GERMANY

• The Act on the Reform of the Market for Medicinal Products (AMNOG) introduced abenefit assessment process in 2011

• The Act created requirements for comparator-driven evidence and altered the pricingprocess to include discount negotiations

• In addition, AMNOG covers products already on the market, opening up the possibility of price cuts to existing products


INSIGHTS | MEDICAL DEVICES IN GERMANY

In size alone, Germany is one of the most attractive markets for medicaldevices. Recent reforms have extended the criteria for achieving market accessin this innovative sector but understanding their dynamics is essential toharnessing the new potential. Lessons can be drawn from early experience ofthe regulatory changes.

Roger-Axel Greiner, PHD is Senior Consultant RWE Solutions & HEOR, IMS [email protected]

Stefan Plantör, PHD, MBA, MSC is Director RWE Solutions & HEOR, IMS [email protected]

The authors



Understanding the impact of regulatory changeLong established as a principal producer of high-quality medical equipment anddiagnostics, Germany’s medical devices market reached an estimated US$23.2 billionin 2012 making it the largest in Europe and the third largest in the world next to theUS and Japan. Although the local manufacturing industry is strong, about 75% of thismarket is accounted for by imports.1

Despite the appeal of the medical devices market in Germany, there are sectoral differences specific to the Germanhealthcare system that directly impact the reimbursement of innovative medical procedures and products by thestatutory health insurance (SHI) funds. For the vast majority of the German population, these funds are key todetermining how the money is allocated and how services are provided in the healthcare system. With new regulationsnow coming into play, what do these dynamics mean for this highly innovative sector?

CURRENT REIMBURSEMENT OF MEDICAL DEVICES: A TALE OF TWO SETTINGSThe peculiarity of the German healthcare system in its very clear division of inpatient and outpatient care in the SHI isreflected in two quite opposite approaches to reimbursing medical devices.

Inpatient sectorThe principle of “permission unless explicitly banned” (Erlaubnis mit Verbotsvorbehalt) refers to the provision of newmethods in the inpatient sector and is defined in Social Code book V (SGb V) § 137c. This covers the assessment ofdiagnostic and treatment methods in the hospital. The decision to use an innovative method rests mainly with thehospital doctor. After uptake in the inpatient sector, the innovation is in most cases reimbursed according to one of theGerman diagnosis-related groups. Only if the Federal Joint Committee (FJC, Gemeinsamer bundesausschuss G-bA) hasexplicitly excluded the method following an evaluation is it not covered by SHI funds.

Outpatient sectorHere, the principle is “banned until explicitly permitted” (Verbot mit Erlaubnisvorbehalt) according to § 135 SGb Vwhere diagnostic and treatment methods are required to be of benefit, medical necessity and efficiency. Therefore, newmethods must be assessed before they can be admitted to the catalogue of services. Medical procedures are onlyreimbursed in the outpatient sector if the FJC has made a positive decision.

In the case of a positive decision, the FJC publishes a directive recommending admission of the method to the Physicians' Fee Schedule (PFS, Einheitlicher bewertungsmaßstab EbM) catalogue, and the Valuation Committee(bewertungsausschuss) then determines the value of the new method relative to other methods already listed in the PFS.

In the opposite case, if the FJC disclaims the innovative method, a directive is published adding the procedure to theexclusion list or “negative list” (in Annex II of the Guidelines on SHI-accredited Outpatient Methods) and the method isnot covered by SHI funds.

NEW EXTENSION OF REIMBURSEMENT CRITERIAThe law on SHI Care Structure (GKV-Versorgungsstrukturgesetz, GKV-VStG), introduced in § 137e SGb V in 2012, extendsthe reimbursement criteria of new methods in both the inpatient and outpatient sectors. This means that for methodsof diagnosis and treatment whose benefits have not yet been sufficiently demonstrated but which reveal the potentialfor a required alternative treatment, the Federal Joint Committee (FJC, Gemeinsamer bundesausschuss G-bA) can, infuture, decide on a directive to test.

MEDICAL DEVICES IN GERMANY | INSIGHTS

A new era for medical devices in Germany?



INSIGHTS | MEDICAL DEVICES IN GERMANY


Conditional coverage for inpatient sectorbriefly, the new § 137e SGb V grants the possibility that an application for testing a new method for diagnosis ortreatment can be made to the FJC independently of the advisory procedure according to § 135 or § 137c SGb V. Infuture, in the inpatient sector, a new method without proven evidence will not be excluded from inpatient care until itspotential has been evaluated. Only new methods that are recognizably harmful or inappropriate will be excludedwithout evaluation. The SHI will ensure coverage during the assessment period. In the outpatient sector, the admissionof a new method in the ambulatory reimbursement system will continue to depend solely on the FJA decision afterevaluation of benefit, medical necessity and efficiency.

Trial phase for evidence developmentWhat else is new with § 137e SGb V? In accordance with § 135 and § 137c SbG V, only impartial members of the FJC ororganizations representing patients in the FJC were eligible to apply for the examination and assessment of a method,apart from the top associations of the care providers and the health insurances.

With § 137e SGb V, eligible applicants include, for the first time, manufacturers of a medical device and companies thathave in any other way an economic interest in providing a new technical application at the expense of SHI. Moreover,the manufacturers have the opportunity to conduct a study which is in accordance with the FJC’s requirements, thecosts of which will be partially funded on the basis of company size and revenues.

During the trial phase, the new method is already covered by the SHI funds and as soon as the scientific evaluation iscompleted, the FJC decides whether the method is definitely reimbursed and publishes a directive in the federalbulletin accordingly (bundesanzeiger).

From the perspective of the FJC, the new regulations fortesting will improve the conditions for generatingevidence which is needed for its decisions. Testing meanstemporary suspension of the benefit assessment andlimited permission for providing the method under thedirective for testing.

The FJC is responsible for the formal examination of theapplication for testing and commissions the impartialInstitute for Quality and Efficiency in Health Care (IQWiG)to evaluate the content.

In summary, the important change (§ 137c in connectionwith § 137e SGb V) is the provision of a directive for testingbefore exclusion of an inpatient service. The new § 137eSGb V considers only innovative methods that show apotential benefit, whereas methods without potential areexcluded (Figure 1).

PRACTICAL APPLICATION: BENEFIT ASSESSMENTS OF PET AND PET/CT One interesting example of how the evaluation process actually maps for public reimbursement is IQWiG’s benefitassessments of positron emission tomography (PET), alone or in combination with computed tomography (CT).

IQWiG investigated the benefit of PET and PET/CT in 10 indications. According to the health technologies assessmentmethodology, the level of evidence of clinical studies has been proven, presenting randomized controlled trials (RCTs) andmeta-analyses of RCTs with the highest level of evidence. RCTs have to be designed with the standard therapy as comparatorin order to create the potential to directly demonstrate the additional benefit. The additional benefit results from the impactof the medical therapy on patient-relevant endpoints that are specified in the IQWiG Methodology paper 4.0:

• Improvement in the state of health • Shorter duration of illness • Extension of life • Reduction of side effects • Improvement in quality of life

fIGURE 1: SCHEMATIC fLOW Of METHOD EVALUATION fORINPATIENT SECTOR

EVALUATION CRITERIA:BENEFIT – MEDICAL NECESSITy – EFFICIENCy

No sufficientevidence for

benefit but studiesongoing

No evidence forbenefit and no

potential

No sufficientevidence but

showing potential

In compliancewith criteria

Inclusion Suspension Testing forevidence

due to § 137e SGB V

Exclusion

The results of the IQWiG benefit dossiers are sobering (Table 1). In all 10 assessments, IQWiG stated that the clinical datadid not allow robust conclusions related to the advantages and disadvantages of using PET or PET/CT, for example,there was no proven benefit. This is because none of the studies directly compared the benefit of these imagingtechniques with conventional diagnostics: in 10 of 10 indications, RCTs – the standard requirement to prove benefit –were missing. Furthermore, IQWiG’s principal criticism was that patient-relevant endpoints were not included in thestudies (Table 2).

The three key learnings from the IQWiG PET assessment for the clinical study design concern RCTs, standard of care ascomparator and patient-relevant outcomes (Table 3). For manufacturers of medical devices, this underscores theincreasing importance of reviewing and verifying the evidence for products and starting early to close the gaps.

CONCLUSIONOverall, with the introduction of § 137e in the Social Code book V, the FJC has expanded its scope of action. The newtesting option allows for evidence to be generated on the basis of the potential in new methods. As all stakeholdersbreak new ground, the reform might be regarded as a learning system. It is still too early to estimate the effects of theregulations but their close and continued evaluation will be important. •

MEDICAL DEVICES IN GERMANY | INSIGHTS


TABLE 1: RESULTS Of IQWIG BENEfIT ASSESSMENTS Of PET IN 10 INDICATIONS

1 Espicom. The Medical Device Market: Germany, 2012. www.espicom.com/germany-medical-device-market. Accessed on April 19, 2013

Part of assessment IQWiG conclusion Number of reports

Patient-relevant benefit Proof of benefit. 0

No evidence of patient-relevant benefit. 10

Diagnostic (and prognostic)accuracy

Improved diagnostic accuracy in comparison to standard diagnostic procedures. 2

Improved diagnostic accuracy only for subgroup, in either restaging or recidive staging, or in comparison againstsimple/cheap comparator. 3

No evidence of improved diagnostic accuracy. 5

No patient-relevantoutcomes (PROs) 9 (90%) PROs according to G-BA/IQWiG are mortality, morbidity, quality of life,

safety/reduction of side-effects and must be implemented in a study to show benefit.

TABLE 2: MAJOR CRITICISMS fROM IQWIG BENEfIT ASSESSMENTS Of PET IN 10 INDICATIONS

Critical Number of indications (% of all 10 indications) GAP analysis

No randomized controlledtrials (RCTs) 10 (100%)

RCTs are the standard requirement to prove benefit. An RCT for PET should be planned in an indication where PET has shown improved diagnostic accuracy andwhere a good treatment therapy is available. In special cases the study must notmandatorily be a standard RCT.

Comparator

The comparator has to be identified according to benchmarks derived from theinternational standards of evidence-based medicine (standard of care diagnosticprocedure). If there are several alternatives, the more economictherapy/diagnostic procedure is selected.

Systematic review of treatment guidelines with high levelof evidence.

Patient-relevant outcome

The benefit of a medical device is the patient-relevant therapeutic effect, inparticular in respect of: the improvement in state of health; reduction of duration of the disease; longer survival; reduction in side-effects; or improvement in quality of life.

IQWIG Methodenpapier 4.0

TABLE 3: KEY LEARNINGS fROM IQWIG PET ASSESSMENT fOR CLINICAL STUDY DESIGN

Best practice Description Recommended methodology

Randomized controlled trial Patients, after assessment of eligibility and recruitment, but before theintervention to be studied begins, are randomly allocated to receive one or otherof the alternative treatments under study.

CONSORT (Consolidated Standards of Reporting Trials)

Today, more than ever, pharmaceutical manufacturers aredependent on insights from real-world evidence to support thevalue of their medicines. Especially for mature products facinggrowing competition, these may be key to affirming theircontinued and appropriate use in recommended protocols ofcare. Robust and validated economic models can play a criticalrole in this process, affording a deeper understanding of costand outcomes in the local clinical setting. For one leadinginnovative company with a specialty in personalized healthcare,the ability to develop a compelling value case leveraging newobservational evidence was pivotal to the repositioning of an in-market brand for chronic hepatitis b (CHb) in Italy, based on itsproven cost-effectiveness versus alternative available treatments.

OPTIMIZING HEPATITIS B TREATMENTHepatitis b virus (HbV) infection is a serious global healthconcern, with two billion infected individuals and more than 350million having active disease. Although its incidence has beendrastically reduced through vaccination, CHb still presents alarge burden due to the high associated risk of cirrhosis and livercancer.

In CHb, the progression of liver disease is essentially due toongoing viral replication. Inhibiting this replication is thus themain goal of treatment. The two available pharmacologicalapproaches are a finite 48-week course of pegylated interferon(PEG-IFN) or continuous administration of nucleoside analogues(NAs). PEG-IFN works by stimulating host immunity and caninduce the sustained immune control of HbV infection inresponsive patients after the end of treatment. Conversely,therapy with NAs centers on the direct inhibition of viralreplication; patients must continue treatment indefinitely sincethey are unable to achieve sustained immune control of HbVinfection following withdrawal, even after years of continuousadministration. The two most novel and more effective NAs areentecavir (ETV) and tenofovir (TDF).

In Europe, PEG-IFN is licensed for the treatment of CHb andchronic hepatitis C and within current European guidelines1 it isthe recommended first-line pharmacotherapy for both variantsof CHb (HbeAg-positive and -negative). However, this approachappears to have only limited application in Italian clinicalpractice. This is possibly due to the relatively low antiviral effectof PEG-IFN at the end of the 48-week course, and the inferiortolerability that may lead clinicians to commence treatmentdirectly with NAs (Figure 1).

Sophisticated simulationmodeling reveals importantcost savings for productrepositioning leveragingemerging new clinicalevidence

Demonstrating cost-effectivenessof an individualized approach tochronic hepatitis b treatment inItaly

The authorsSergio Iannazzo, MEng, MBA is Director RWE Solutions & HEOR, IMS [email protected]

Maria De Francesco, MSCis Analyst RWE Solutions & HEOR, IMS [email protected]

PROJECT FOCUS | CHRONIC HEPATITIS b


LEVERAGING NEW CLINICAL EVIDENCEIt was against this background that the companyapproached IMS to help improve the visibility of itsmature PEG-IFN in Italy with a view to re-launching theproduct in this market. The move was triggered by theemergence of new evidence from a number of cohortstudies,2,3,4,5 which demonstrated a correlation betweenspecific virologic/serologic markers at week 12 oftreatment with PEG-IFN and the absence of response atthe end of the 48-week course. Despite the observationalnature of these findings, they nevertheless paved the wayfor early identification of non-responders to the drug.

Adoption of the stopping rule at week 12 thus created theopportunity to improve the efficient allocation ofresources, allowing non-responders to discontinue PEG-IFN early without the need to complete the full course(Figure 2).

Working in collaboration with the company’s marketaccess and marketing teams, IMS was tasked withidentifying the potential of the 12-week stopping rule toreduce the cost per responsive patient and positivelyimpact the value of PEG-IFN. This was particularly validgiven growing payer concerns about the expense of NAsfor CHb and the appeal of newer drugs in this class thatcould eliminate the viral load. National payers were wellaware that respect of the clinical guidelines would easilyreduce current expenditure for CHb. Thepharmacoeconomic evaluation could thus serve as auseful tool to demonstrate the extent of this saving.

MODELING THE IMPACT OF ALTERNATIVESTRATEGIESFor the purpose of the economic analysis, IMS developeda Markov model to compare, over the lifetime horizon,HbeAg-negative CHb treatment strategies, consistingeither of first-line PEG-IFN treatment with the stoppingrule and potential switch to the current most effectiveNAs (eg, ETV, TDF), or NA treatment first-line. The cost-effectiveness analysis focused on HbeAg-negative CHb,the most common form of the disease in Italy.

The approach involved first a systematic literature reviewof published randomized controlled trials (RCTs) and newobservational evidence to identify the most reliable andcurrent clinical data for populating the model. Measuredoutcomes were average survival, quality-adjusted lifeyears and costs, calculated from the Italian NationalHealth Service (SSN) perspective. To account for all themost common stages of disease progression, ten healthstates were included in the model: active CHb; virologistresponse; s-antigen clearance; compensated cirrhosis withactive CHb; compensated cirrhosis with virologistresponse; decompensated cirrhosis; hepatocellularcarcinoma; liver transplant; post-liver transplant; anddeath (Figure 3 overleaf ). Probabilities associated withtreatment efficacy were extrapolated from related RCTsand long-term observational studies. Responseprobabilities for PEG-IFN were adjusted to account foradoption of the stopping rule.

fIGURE 1: KEY ATTRIBUTES Of PEG-IfN AND NUCELOSIDEANALOGUES IN CHB

CHRONIC HEPATITIS b | PROJECT FOCUS


fIGURE 2: APPLICATION Of THE STOPPING RULE IN PEG-IfNTREATMENT

Serologic/virologicmarkers

NAs• Potential antiviral

effect• Good tolerance• Oral adminstration

• Indefinite duration• Risk of resistance• Unknown

long-term safety

(PEG-)IFN• Finite duration• Absence of resistance• Higher rates of anti-

Hbe and anti-Hbsseroconversion with 12 months of therapy

• Moderate antiviraleffect

• Inferior tolerability• Risk of adverse events• Subcutaneous injections

Advantages

Disadvantages

Source: European Association for the Study of the Liver. EASL Clinical PracticeGuidelines: Management of chronic hepatitis B virus infection. J Hepatology, 2012;57: 167-185

Complete PEG-IFN course

StoppingRule

StartPEG-IFN

DiscontinuePEG-IFN

Wk12

Wk48


DEMONSTRATING AND COMMUNICATING COST-EFFECTIVENESSbase-case results from the analysis showed that treatmentwith first-line PEG-IFN using the stopping rule was highlycost-effective compared to the use of NAs first line.Through its work with IMS in the development of asimulation model for the economic evaluation of CHbtreatment strategies, the company thus gainedcompelling proof that the cost-effectiveness of HbeAg-negative CHb therapy in Italy could be improvedsignificantly using first-line PEG-IFN with the adoption ofthe week-12 stopping rule. This has enabled thedevelopment and dissemination of key value messages tosupport the re-positioning of the product in Italy. Theevidence, together with the model methodology, hasbeen presented at two major conferences,6,7 and acceptedfor publication in a leading international journal invirology and infectious diseases.8 IMS has also supportedthe development of a dynamic, simplified version of thecost-effectiveness model for use on an App for iPad whichwill further enhance visibility of the drug across a broadaudience of stakeholders. •

1 European Association for the Study of the Liver. EASL Clinical Practice Guidelines: Management of chronic hepatitis b virus infection. J Hepatology, 2012; 57: 167-1852 Rijckborst V, Hansen bE, Cakaloglu Y, Ferenci P, Tabak F, Akdogan M, et al. Early on-treatment prediction of response to peginterferon alfa-2a for HbeAg-negative chronic

hepatitis b using HbsAg and HbV DNA levels. Hepatology, 2010; 52:454–4613 brunetto MR, Moriconi F, bonino F, Lau GK, Farci P, Yurdaydin C, et al. Hepatitis b virus surface antigen levels: a guide to sustained response to peginterferon alfa-2a in

HbeAg-negative chronic hepatitis b. Hepatology, 2009 Apr; 49(4):1141-504 Marcellin P, Piratvisuth T, brunetto M, bonino F, Farci P, Yurdaydin C, Gurel S, Kapprell H-P, Messinger D, Popescu M. On-treatment decline in serum HbsAg levels predicts

sustained immune control 1 year post-treatment and subsequent HbsAg clearance in HbeAg-negative hepatitis b virus-infected patients treated with peginterferon Alfa-2a [40KD] (Pegasys). 20th Conference of the Asian Pacific Association for the Study of the Liver (APASL), beijing, China, 25-28 March, 2010

5 Rijckborst V, Hansen bE, Ferenci P, brunetto MR, Tabak F, Cakaloglu Y, et al. Validation of a stopping rule at week 12 using HbsAg and HbV DNA for HbeAg-negative patientstreated with peginterferon alfa-2a. J Hepatol, 2012 May; 56(5):1006-11

6 Iannazzo S, Espinós b, Coco b, brunetto M, Rossetti F, Caputo A, bonino F. Cost-effectiveness of an individualized approach in the treatment of HbeAG-negative CHbpatients with peginterferon alfa-2A in Italy. ISPOR 15th Annual European Congress, berlin, Germany, 3-7 November, 2012

7 Iannazzo S, Coco b, brunetto MR, Rossetti F, Caputo A, bonino F. Cost-effectiveness of peg-interferon alfa-2a therapy of HbeAg-negative chronic hepatitis b in Italy using apersonalized approach based on week 12 HbV-DNA and HbsAG stopping rule. 63rd Annual Meeting of the American Association for the Study of Liver Diseases (AASLD),boston, MA, USA, 9–13 November, 2012

8 Iannazzo S, Coco b, brunetto MR, Rossetti F, Caputo A, Latour A, Espinos b, bonino F. Individualized treatment of HbeAg-negative CHb using peg-interferon alfa-2a as first-line and week 12 HbV-DNA\HbsAg stopping rule. A cost-effectiveness analysis. Antivir Ther 2013, Mar 13. doi: 10.3851/IMP2555. [Epub ahead of print]

fIGURE 3: COST-EffECTIVENESS MODEL STRUCTURE8

All-cause mortality is also taken into consideration in every healthstate (not shown), defined as death for any cause which is notdirectly attributable to CHb.

Key: CHb-A: Active Chronic Hepatitis b; CHb-R: Chronic Hepatitis bwith virologic response; sCL: Clearance of HbsAg; CC-A:Compensated Cirrhosis with active CHb; CC-R : CompensatedCirrhosis with virologic response; DCC: Decompensated Cirrhosis;HCC: Hepatocellular Carcinoma; LT: Liver Transplant.

PROJECT FOCUS | CHRONIC HEPATITIS b


Death

sCL

CHb-A

CC-A CC-R

DCC HCC

LT Post LT

CHb-R

RHEUMATOID ARTHRITIS | PROJECT FOCUS


A number of specific therapy areas continue to see new marketentrants that obscure real-world comparative effectiveness. As aresult, some payers and HTAs are avoiding effectiveness-baseddecisions altogether, passing responsibility for treatmentdecisions directly to physicians and patients. The patient accessscheme approved by NICE for UCb’s Cimzia (certolizumab), abiologic disease-modifying anti-rheumatic drug (DMARD) for thetreatment of rheumatoid arthritis (RA), is a case in point.1

The scheme is based on findings from UCb’s phase III trials thatresponse at 12 weeks of therapy is predictive of response at 52 weeks of therapy. UCb has agreed to bear the cost of therapyfor the first three months, with the UK National Health Serviceassuming responsibility thereafter. While the scheme wasexplicitly proposed as not requiring clinician input on continuingtherapy past three months, it is reasonable to expect that mostphysicians who are aware of it will evaluate whether theirpatients will benefit from continued therapy at the point wherefinancial responsibility shifts from manufacturer to payer. Underthis precedent-setting scheme, the HTA market access decision isessentially repeated for every patient rather than for the payer’sbudget as a whole. What evidence will physicians and patientsuse to predict the chances of future benefit in light of thepatient’s limited experience on treatment?

MEASURING TREATMENT OUTCOMESSome therapy areas, for example diabetes, have effectivenessendpoints such as HbA1c that are accessible and systematicallymeasurable. Where crowded therapy areas have such clearlymeasurable outcomes, physicians and patients can makeeffectiveness decisions based on unambiguous clinical evidence.In addition, payers could verify whether treatments were beingchosen consistent with clinical evidence and guidelines, becausethey could more easily access real-world evidence (RWE) tocompare those treatment choices against outcomes.

In RA, however, the situation is less clear. RA is a progressiveautoimmune disease, where the primary measures of outcomeare either patient-reported or physician-rated markers of pain,joint tenderness and disability. Diagnostics do exist, but in theform of joint imaging studies that must still be interpreted by aclinician for signs of progressive joint damage. This reduces thelikelihood that physicians and patients will have truly objectiveratings of outcome on which to base treatment effectivenessdecisions. It also limits their ability to benchmark performanceagainst that of other patients, or for payers to review theconsistency of treatment decisions against clinical evidence.

The development ofvalidated proxy measures foroutcomes, leveraging largehealthcare datasets, canenable evidence-baseddecisions in therapy areaswhere measurable endpointsare not easily accessible

Treatment pathway decisionsupport using real-worldevidence from largepopulations

The authorsVernon Schabert, PHD is Senior Principal RWE Solutions & HEOR,IMS [email protected]

Jason yeaw, MSis Director RWE Solutions & HEOR, IMS [email protected]

Jon Korn, BSis Statistical Programmer RWE Solutions& HEOR, IMS [email protected]



PROJECT FOCUS | RHEUMATOID ARTHRITIS

If payers and HTAs delegate treatment decisions tophysicians and patients in these or other therapy areas,they are also ceding influence on the evidence base usedby those stakeholders to make treatment decisions. Whenthe outcomes of care are measured only with substantialeffort, how can payers and HTAs trust that treatmentdecisions will be consistent with the evidence-baseddecision making that the payer might prefer? And intherapy areas with many available treatments, how canpayers assure that adequate observational data areavailable to provide an adequate evidence base?

RHEUMATOID ARTHRITISThis is precisely the challenge that manufacturers of RAtherapies face in today’s market. After many years of thebiologic DMARD market being shared by etanercept,infliximab and then adalimumab, six new products havereceived FDA or EMA approval since 2006 (abatacept,certolizumab, golimumab, rituximab, tocilizumab,tofacitinib). Another product has been approved forrelated autoimmune conditions also treated by the firstthree agents in market (ustekinumab, in 2009).2

Outcomes in RA are frequently measured by countingswollen and tender joints. The DAS-28 is an often-usedclinician rating instrument for this purpose and is a criticalcomponent of other commonly-cited outcome measures.3Effective treatments reduce the number of swollen andtender joints experienced by patients. Initial response canoccur in a few weeks, but treatments vary in how longpatients perceive continued benefit. Some courses oftreatment are characterized by early discontinuation orswitching of therapy, increased dosing, orsupplementation with traditional DMARDs orcorticosteroids in order to maintain the level of response.

Professor Jeff Curtis of the University of Alabama-birmingham validated a proxy measure for estimating theeffectiveness of these biologic DMARDs over a one-yearperiod.4 In his algorithm, treatment changes such asdosing titration, inadequate persistence or adherence,and the addition of other therapies were signs of non-effective treatment. These treatment changes correlatedstrongly with inadequate response as measured by theDAS-28 among VA patients enrolled in an RA registry.

Application of an algorithm such as this can empowermultiple stakeholders to estimate outcomes using moreaccessible data and measures. DAS-28 scores are timeconsuming to calculate and many clinicians and patientsrely on quicker assessments in routine clinical practice.This means that few payers, physicians or patients wouldhave routine access to real-world outcomes for a widepopulation of RA patients. However, establishing thevalue of changing treatment patterns as a predictor ofoutcomes enables all stakeholders to judge effectivenesswith widely-accessible treatment pattern data, and canguide a physician and patient through treatmentassessment based on the patient’s own treatment history.

EXTENDING THE EVIDENCE BASE IMS applied the Curtis algorithm to evaluate treatmenteffectiveness for a wide range of biologics using IMSLifeLink PharMetrics Plus™. Two of the analyses haveformed the basis of research presentations at ISPOR,5,6

with others scheduled for submission to journals andpresentation at other conferences.

The use of such a large payer database enabled thedevelopment of effectiveness estimates for many of thenewer biologic DMARDs. Given the dominance ofadalimumab, etanercept and infliximab for many years, far

fewer real-world studies haveexamined the treatment patterns andeffectiveness of the more recenttreatment alternatives. Starting fromover 220,000 patients with evidence ofa biologic DMARD between 2007 and2010, it was possible to constructmeaningful treatment cohorts formany agents that had not been widelystudied before (Figure 1).

The findings confirmed those in theoriginal validation study; about 28% ofpatients avoided the dosing changes,poor adherence, or therapymodifications that were consideredsigns of failed effectiveness in thealgorithm.

fIGURE 1: SUPPORT fOR MULTI-LINE THERAPY COHORTS IN CROWDED MARKETS WITHIMS PHARMETRICS PLUS™

First year CohortAbatacept (n=1,160)Adalimumab (n=4,991)Certolizumab (n=138)Golimumab (n=261)Infliximab (n=2,352)Etanercept (n=7,247)

Second year CohortAbatacept (n=1,156)Adalimumab (n=620)Infliximab (n=229)Etanercept (n=994)

Switch CohortAbatacept (n=196)Adalimumab (n=527)Certolizumab (n=22)Golimumab (n=60)Infliximab (n=202)Etanercept (n=318)


RHEUMATOID ARTHRITIS | PROJECT FOCUS

The application of this algorithm was then extended onestep further. In a typical HEOR cohort analysis, measuringan outcome over a fixed time period is the end of theanalysis. However, physicians and patients will continue tostruggle with treatment choices for the duration of thepatient’s condition. A follow-on study thus set out todetermine what happens among patients who (a)continue on therapy a second year after a first year ofsuccessful therapy, or (b) switch to a second therapy sometime in their first year.

These second-line analyses yielded ample evidence thatpatient experience with initial therapy would predictexperience on future therapies. First year “effectiveness”,as defined by the algorithm, was about 28% across alltreatments. but of those who continued effective therapy(and who, by definition, were 100% effective in year one),nearly 47% experienced effectiveness for the entiresecond year. In contrast, among those who switched fromtheir initial therapy within the first year to a secondbiologic DMARD, only 20% experienced effective therapyfor a year following the switch to a new agent.

The predictability of future therapy outcomes extended tothe individual behaviors that made up the effectivenessalgorithm. As shown in Figure 2, 17% of patients switched

therapies in their first year. Among those whoexperienced effectiveness over the first year, second-yearswitch rates dropped to 7%. Among those who switchedonce, switch rates to a third biologic increased to 28%.

Drug-specific characteristics also seemed to followpatients to future therapies. Several studies havepreviously shown that infliximab is associated with highrates of dose increase during the first year of therapy.7,8

This analysis showed the same, with 46% of infliximabpatients increasing their dose during first-year therapy,compared to only 10% of first-year abatacept patients. butmost patients who switched to abatacept for second-linetherapy had started infliximab as their first-line therapy.After the switch, the rate of dose increase amongabatacept patients climbed to 19%, as if the priorexperience with infliximab was a marker for future risk ofdose increases.

CONCLUSIONSMarket pressures such as personalized medicine, payment-for-performance and value-based contracting will continueto increase pressure on stakeholders other than payers tomake evidenced-based treatment decisions. Payers havegrown accustomed to using RWE to benchmark and guidethose decisions although this has occurred mainly wheretherapy areas have measurable and widely accessibleoutcomes in RWE sources. These analyses demonstrate howsuch decision making can be extended to therapy areaswithout accessible outcome measures, through thedevelopment of validated proxy measures for outcome. The insights gained suggest that alternative framing ofanalytics from large RWE sources could ensure greateraccess to evidence-based insights for non-payerstakeholders at various milestones in the patient’s course ofillness. •

1 Certolizumab pegol (CIMZIA®) for the treatment of Rheumatoid Arthritis. Patient access scheme (pas) submission to NICE, 23 July 2009 (approved 21 January 2010).http://www.nice.org.uk/nicemedia/pdf/Cimzia%20PAS%20submission.pdf; accessed May 2013

2 IMS KnowledgeLink, May 20133 Prevoo MLL, Hof van ‘t MA, Kuper HH, Leeuwen van MA, Putte van de LbA, Riel van PLCM Modified disease activity scores that include twenty-eight-joint counts:

Development and validation in a prospective longitudinal study of patients with rheumatoid arthritis. Arthritis Rheum. 1995, 38:44-484 Curtis JR, et al. Derivation and preliminary validation of an administrative claims-based algorithm for the effectiveness of medications for rheumatoid arthritis. Arthritis

Res Ther. 2011;13(5): R1555 Schabert VF, Yeaw J, Korn JR, Quach C, Harrison DJ, Yun H, Joseph G, Collier D. Algorithm-based estimation of biologic effectiveness for rheumatoid arthritis. International

Society for Pharmacoeconomics and Outcomes Research 18th Annual International Meeting, New Orleans, LA, USA, 18-22 May, 2013 (Poster)6 Schabert VF, Yeaw J, Korn JR, Quach C, Harrison DJ, Yun H, Joseph G, Collier D, Curtis JR. Effectiveness of second line biologic use in rheumatoid arthritis after switching

from first-line biologics. International Society for Pharmacoeconomics and Outcomes Research 18th Annual International Meeting, New Orleans, LA, USA, 18-22 May, 2013(Poster)

7 Schabert VF, bruce b, Ferrufino CF, Globe DR, Harrison DJ, Lingala b, Fries JF. Disability outcomes and dose escalation with etanercept, adalimumab, and infliximab inrheumatoid arthritis patients: A US-based retrospective comparative effectiveness study. Curr Med Res Opin. 2012 Apr; 28(4):569-80

8 Schabert VF, Watson C, Gandra SR, Goodman S, Fox KM, Harrison DJ. Annual costs of tumor necrosis factor inhibitors using real-world data in a commercially insuredpopulation in the United States. J Med Econ. 2012; 15(2):264-75

fIGURE 2: BIOLOGIC DMARD SWITCH RATES OVER ONE YEAR, BASED ON LINE Of THERAPY

30%

20%

10%

0%

17%

7%

28%

Switched from priorDMARD

Second Year(Effective)

First year (All)

Enabling your real-world successIMS offers a spectrum of world-class expertise in real-world evidence (RWE) and healtheconomics & outcomes research (HEOR) to deliver the local excellence you need.

In a future where healthcare efficiency and quality are measured through the lens of ‘real-world’ insights, externalvalidity demands a focus on the right data sources, scientifically credible research and actionable communication.

IMS is committed to helping you succeed:

• Largest multi-disciplinary team of RWE and HEOR experts, based in 18 countries worldwide

• Credible scientific voice and deep therapy area knowledge, captured in over 2400 publications

• Market leadership in developing and adapting robust economic models

• Most advanced capabilities in RWE management and analysis, leveraging relevant IMS proprietaryand other key external, third-party data assets

• Proven expertise in generating and communicating RWE to advance stakeholder engagement at all levels

IMS is a leading independent provider of RWE, outcomes research, economic modeling and market access solutions,and value communication.

Our unique, data-agnostic market position can help you develop and support the evidence required to engage globaland local healthcare stakeholders, with deep insights into product safety, efficacy, cost, value for money and affordability.

IMS RWE SOLUTIONS & HEOR | OVERVIEW


Outcomes Research• Evidence generation• Late-phase studies• Patient-Reported Outcomes (PROs)• Database studies• Mixed methods• Epidemiology • Risk management

Health Economic Modeling• Health economic evaluations• Core models & local adaptations• Budget impact• Meta-analyses• Indirect comparisons• IMS CORE Diabetes Model

Market Access• Value development planning

• Market access strategy

• Core value dossiers & local adaptations

• HTA readiness

• Value communication

• Reimbursement submissions

Technology• Platform engines• Data warehouse/data marts• Encryption systems & linking technology• Meta-data repository• User interface & sophisticated

analytics library• Electronic data capture

IMS LifelinkTM

Largest collection of scientifically validated,anonymized patient-level data assets: • Health plan claims• PharMetrics PlusTM

• Longitudinal Rx• Electronic medical records• Hospital disease• Oncology• Diabetes

Real-World Evidence Solutions• Data sourcing & validation• Data integration & linking• Data management & curation • Platform development• Customized analytics & reporting• Evidence planning

We offer a wide spectrum of solutions

Global scope, local expertiseIMS RWE Solutions & HEOR experts are located in 18 countries worldwide and they havepublished on projects completed in more than 50 countries on all continents.

Dr. Michael NelsonSenior Principal IMS Health1725 Duke Street, Suite 510Alexandria, VA 22314USATel: +1 703 837 [email protected]

Dr. Jacco KejaSenior Principal IMS Health210 Pentonville RoadLondon N1 9JYUKTel: +31 (0) 631 693 [email protected]

Jon ResnickVice President and General ManagerIMS Health1725 Duke Street, Suite 510Alexandria, VA 22314USATel: +1 703 837 [email protected]

Adam LloydSenior Principal IMS Health210 Pentonville RoadLondon N1 9JYUKTel: +44 (0) 20 3075 [email protected]

yOUR PRIMARy CONTACTS

NORTH AMERICAREGIONAL HEADQUARTERS11 Waterview BoulevardParsippany, NJ 07054USATel: +1 973 316 4000

UNITED STATES1725 Duke StreetSuite 510Alexandria, VA 22314USATel: +1 703 837 5150

One IMS DrivePlymouth MeetingPA 19462USATel: +1 610 834 0800

CANADA16720 Route TranscanadienneKirkland, Québec H9H 5M3CanadaTel: +1 514 428 6000

LATIN AMERICAREGIONAL HEADQUARTERSInsurgentes Sur # 23755th Floor, Col. TizapanMexico City D.F. - C.P. 01090 MexicoTel: +52 (55) 5062 5239 or +1 917 542 5844

EUROPEREGIONAL HEADQUARTERS210 Pentonville RoadLondon N1 9JYUnited KingdomTel: +44 (0) 20 3075 4800

BELGIUMMedialaan 381800 VilvoordeBelgiumTel: +32 2 627 3211

FRANCE29ème EtageTour Ariane5-7 Place de la Pyramide92088 La Défense CedexFranceTel: +33 1 41 35 1000

GERMANYErika-Mann-Str. 580636 MünchenGermanyTel: +49 89 457912 6400

ITALYViale Certosa 220155 MilanoItalyTel: +39 02 69 78 6721

SPAINDr Ferran, 25-2708034 BarcelonaSpainTel: +34 93 749 63 00

SWEDENSveavägen 155/Plan911346 StockholmSwedenTel: +46 8 508 842 00

SWITZERLANDTheaterstr. 44051 BasleSwitzerlandTel: +41 61 204 5071

UNITED KINGDOM210 Pentonville RoadLondon N1 9JYUnited KingdomTel: +44 (0) 20 3075 4800

ASIA PACIFICREGIONAL HEADQUARTERS8 Cross Street #21-01/02/03PWC BuildingSingapore 048424Tel: +65 6412 7365

AUSTRALIALevel 5, Charter Grove29-57 Christie StreetSt Leonards, NSW 2065AustraliaTelephone: +61 2 9805 6800

CHINA7/F Central TowerChina Overseas PlazaJianguomenwai Avenue, Chaoyang DistrictBeijing 100001ChinaTel: +86 10 8567 4255

SOUTH KOREA9F Handok Building735 Yeoksam1-dongKangnam-ku Seoul135-755S. KoreaTel: +82 2 3459 7307

TAIWAN8th FloorNo 2, Tun Hwa South RoadSection 1Taipei 10506TaiwanROCTel: +886 2 2721 5337

FOR FURTHER INFORMATION: email [email protected] or visit www.imshealth.com/rwe

IMS RWE Solutions & HEOR office locations

LOCATIONS | IMS RWE SOLUTIONS & HEOR


Health Economic Modeling• Health economic evaluations• Core models & local adaptations• Budget impact• Meta-analyses• Indirect comparisons• IMS CORE Diabetes Model

Expertise in depthIMS has one of the largest global teams of experts in RWE, HEOR and market access of anyorganization in the world.We apply unrivalled experience and specialist expertise to help our clients meet the demands of an increasingly complex global, regional and localpharmaceutical landscape. Our highly qualified, multi-disciplinary consultants and researchers have proven skills and capabilities across all keytherapy areas. Spanning industry, consulting, government and academia, their expertise reflects a global grasp, local experience and a unique,inside market perspective.

Renée J. G. Arnold, PHARMD• Dr. Renée Arnold is Principal RWE Solutions & HEOR at IMS Health with particular expertise in the use of technology to

collect and/or model real-world data to facilitate rational decision making by healthcare practitioners and policy makers. • Renée was previously President and CEO, Arnold Consultancy & Technology where she developed and oversaw outcomes

research for the pharmaceutical, biotech and device industries as well as federal government programs. Her distinguishedcareer in health economics and outcomes research includes roles as President and co-founder of Pharmacon International,Center for Health Outcomes Excellence and Senior VP and Medical Director at William J Bologna International.

• Founding member and former Chair of the Education Committee of ISPOR, Renée has several adjunct appointments andis the author of numerous articles on pharmacoeconomics and cost-containment strategies. She holds a Doctor ofPharmacy degree from the University of Southern California in Los Angeles.

yumiko Asukai, MSC• Yumi Asukai is Principal RWE Solutions & HEOR at IMS Health, specializing in the development of economic models across

the product lifecycle and the interpretation of model outputs for strategic market access and value demonstration. Herexpertise in this field spans from early strategic modeling through to global core cost-utility models.

• Yumi’s background includes roles at Fourth Hurdle Consulting and in healthcare and business consulting in San Franciscoand Tokyo, where she focused on comparative studies of health policies between Japan and the US complemented byanalyses of primary data. Yumi has worked extensively in the cardiovascular, oncology and respiratory disease areas andshe is part of a global modeling taskforce for COPD composed of academic and industry members.

• Yumi holds a Bachelor's degree in Political Science from Stanford University and a Master's degree in Health Policy,Planning and Financing from the London School of Hygiene & Tropical Medicine and the London School of Economics.

Our senior team

Nevzeta Bosnic, BA• Nevzeta Bosnic is Principal at IMS Brogan, where she manages projects to meet the broad spectrum of client needs in the

Canadian pharmaceutical market.• Formerly Director of Economic Consulting at Brogan Inc, Nev has led many strategic consulting, policy and data analyses

for pharmaceutical clients, government bodies and academic institutions in Canada. She has extensive knowledge ofpublic and private drug plans across the country and in-depth expertise and experience on the drug reimbursement process.

• Nev holds a Bachelor’s degree in Business Economics from the School of Economics and Business at the University ofSarajevo, Bosnia-Herzegovina.

Karin Berger, MBA• Karin Berger is Principal RWE Solutions & HEOR at IMS Health with a particular focus on RWE, patient- reported outcomes,

and cost-effectiveness evaluation analyses at a national and international level.• Formerly Managing Director of MERG (Medical Economics Research Group), an independent German organization

providing health economics services to the pharmaceutical industry, university hospitals and European Commission, Karin has more than 15 years experience in the health economics arena. She lectures at several universities, has publishedextensively in peer-reviewed journals, and regularly presents at economic and medical conferences around the world.

• Karin graduated as Diplom-Kaufmann (German MBA equivalent) from the Bayreuth University, Germany, with a specialfocus on health economics.

IMS RWE SOLUTIONS & HEOR | EXPERTISE


Frank-Ulrich Fricke, PHD, MSC• Dr. Frank-Ulrich Fricke is Principal RWE Solutions & HEOR at IMS Health and Professor for Health Economics, Georg-

Simon-Ohm University of Applied Sciences, Nuremberg in Germany, with a focus on health economic evaluations,market access strategies and health policy.

• Formerly a Managing Director of Fricke & Pirk GmbH, and previously Head of Health Economics at NovartisPharmaceuticals, Frank-Ulrich has conducted health economic evaluations across a wide range of therapeutic areas,developing a wealth of experience in pricing, health affairs and health policy. As a co-founder of the NIG 21 association,he has forged strong relationships with health economists, physicians and related researchers working in the Germanhealthcare system.

• Frank-Ulrich holds a PhD in Economics from the Bayreuth University, and an MBA equivalent from the Christian-Albrechts-University, Kiel.

Joe Caputo, BSC• Joe Caputo is Regional Principal RWE Solutions & HEOR, Asia Pacific at IMS Health leveraging more than 20 years

experience in the pharmaceutical sector to help clients address the challenges of global reimbursement and marketaccess throughout the drug development program. He has led numerous projects involving payer research, valuedossiers, local market access models and HTA submissions.

• Joe's background includes industry roles in drug development, sales and marketing, and UK and global health outcomes,as well as consulting in health economics. He has wide-ranging knowledge of the drug development process at bothlocal and international level and a unique understanding of evidence gaps in light of reimbursement and market access requirements.

• Joe holds a BSc in Applied Statistics and Operational Research from Sheffield Hallam University, UK.

David Grant, MBA• David Grant is Senior Principal RWE Solutions & HEOR at IMS Health, specializing in reimbursement and market access,

environmental analysis, prospective and retrospective data collection and communications for product support.• A co-founder and former Director of Fourth Hurdle, David’s experience spans more than 10 years in health economics and

outcomes research consulting, and 15 years in the pharmaceutical industry, including roles in clinical research, newproduct marketing and health economics in the UK and Japan.

• David holds a degree in Microbiology and an MBA from the London Business School.

Joshua Hiller, MBA• Joshua Hiller is Senior Principal RWE Solutions at IMS Health, supporting the strategic planning and development of IMS

capabilities for data sourcing, integration, analytics and studies. He is also currently serving as Alliance Director in the IMScollaboration with AstraZeneca for the advancement of RWE.

• During a career that includes roles in market analytics, government and healthcare consulting in both the US and UK,Joshua has led a wide range of projects for clients in the pharmaceutical and biotech sector as well as industryassociations. He has extensive experience in pharmaceutical pricing, contracting, market landscape development, supplymanagement, cross border trade, lifecycle management, competitive defense, generics market drivers and accountmanagement, with expertise across US and European markets.

• Joshua holds an MBA (Beta Gamma Sigma) from Columbia Business School, New York, and a BS in Mathematics fromJames Madison University, Virginia.

EXPERTISE | IMS RWE SOLUTIONS & HEOR


Benjamin Hughes, PHD, MBA, MRES, MSC• Dr. Ben Hughes is Senior Principal RWE Solutions at IMS Health, leading the development of the IMS RWE service offering.

He has helped many clients in the pharmaceutical industry to articulate and implement their RWE strategies, throughdefinition of RWE vision, business cases for RWE investments, capability roadmaps, partnerships, brand evidence reviews,HEOR function design, RWE training programs and related clinical IT strategies.

• Previously head of the European RWE service line at McKinsey & Co, Ben has extensive experience advising healthcarestakeholders on health informatics and RWE-related topics. This includes work on France’s electronic health recordstrategy, EMR adoption strategy for governments across Europe and Asia, data releases to support the UK’s transparencyagenda, and the development of payer health analytics and RWE capabilities across countries in Europe.

• A widely published author on health informatics, Ben holds Masters’ degrees in Research from ESADE Barcelona and inPhysics from University College, London, an MBA from HEC Paris, and a PhD in Medical Informatics from ESADE Barcelona.


Mark Lamotte, MD• Dr. Mark Lamotte is Principal RWE Solutions & HEOR at IMS Health with responsibility for the content and quality of all

health economic evaluations conducted by his team. • A physician by training (cardiology), Mark spent a number of years in medical practice before joining Rhône-Poulenc Rorer

as Cardiovascular Medical Advisor and later becoming Scientific Director at the Belgian research organization, HEDM. Hehas since worked on more than 150 projects, involving expert interviews, patient record reviews, extensive modeling andreport writing across a wide range of therapy areas, and authored many peer-reviewed publications.

• Mark holds an MD from the Free University of Brussels (Vrije Univeristeit Brussel, Belgium) and is fluent in Dutch, French,English and Spanish.

Won Chan Lee, PHD• Dr. Won Chan Lee is Principal RWE Solutions & HEOR at IMS Health, specializing in prospective and retrospective health

economics research.• Over the course of his career, Won has completed numerous international economic evaluations employing a variety of

analytical methods across a range of diseases and geographies. His expertise includes econometric database analysis,quality of life assessment and advanced economic modeling to establish the economic and humanistic value of new andexisting therapeutic interventions.

• Won holds a Master’s degree in Economics from the University of Grenoble II, and a PhD in Economics from the GraduateCenter of the City University of New York.

Claude Le Pen, PHD• Dr. Claude Le Pen is a member of the strategic committee of IMS Health and Professor of Health Economics at

Paris-Dauphine University, providing expert economic advisory services to the consulting practice.• A renowned economist, leading academic, and respected public commentator, Claude has served as an appointed senior

member of several state commissions in the French Ministry of Health and is an expert for a number of parliamentarybodies, bringing a unique perspective and unparalleled insights into the economic evaluation of pharmaceuticaltechnologies at the highest level.

• Claude studied Business Administration in HEC Business School in Paris and holds a PhD in Economics from Panthéon-Sorbonne University.

Jacco Keja, PHD• Dr. Jacco Keja is Senior Principal RWE Solutions & HEOR at IMS Health, drawing on deep expertise in global market access,

operational and strategic pricing, and health economics and outcomes research. • Jacco’s background includes four years as global head of pricing, reimbursement, health outcomes and market access

consulting services at a large clinical research organization and more than 13 years experience in the pharmaceuticalindustry, including senior-level international and global roles in strategic marketing, pricing and reimbursement andhealth economics.

• Jacco holds a PhD in Biology (Neurophysiology) from Vrije Universiteit in Amsterdam, a Masters in Medical Biology, and an undergraduate degree in Biology, both from Utrecht. He is also visiting Professor at the Institute of Health Policy & Management at Erasmus University, Rotterdam.

Tim Kelly, MSC, BS• Tim Kelly is Vice President RWE Solutions at IMS Health, with responsibility for the company’s RWE data assets and data

architecture backbone, and for overseeing platform delivery infrastructure and engagements to ensure at-scale, high-quality data mart deployment. He also leads the client services team supporting data and technology applications.

• Tim’s background includes two decades of life-science experience managing large-scale data warehousing, technology,and analytic applications and engagements. He has worked with many clients in the pharmaceutical and biotech sectors,leveraging deep expertise in information management and modeling, commercial operations and analytics, advancedanalytics, business intelligence, data warehousing and longitudinal analytics.

• Tim holds a Bachelor’s degree in Quantitative Business Analysis from Penn State University and a Master’s degree inManagement Science from Temple University, Philadelphia.


Adam Lloyd, MPHIL, BA• Adam Lloyd is Senior Principal RWE Solutions & HEOR at IMS Health, with a particular focus on economic modeling and

the global application of economic tools to support the needs of local markets.• A former founder and Director of Fourth Hurdle, and previously Senior Manager of Global Health Outcomes at

GlaxoWellcome, Adam has extensive experience leading economic evaluations of pre-launched and marketed products,developing submissions to NICE and the SMC, decision-analytic and Markov modeling, and in the use of health economicsin reimbursement and marketing in continental Europe.

• Adam holds an MPhil in Economics, and a BA (Hons) in Philosophy, Politics and Economics from the University of Oxford.


Frédérique Maurel, MS, MPH• Frédérique Maurel is Principal RWE Solutions & HEOR at IMS Health, with a particular focus on observational research and

health economics studies.• A skilled consultant and project manager, Frédérique has extensive experience in the economic evaluation of medical

technologies gained in roles at ANDEM, Medicoeconomie, and AREMIS Consultants.• Frédérique holds a Master’s degree in Economics – equivalent to an MS – and completed a post-graduate degree

equivalent to an MPH with a specialization in Health Economics at the University of Paris-Dauphine (Paris IX) as well as adegree in Industrial Strategies at the Pantheon-Sorbonne University (Paris I).

Joan McCormick, MBA• Joan McCormick is Principal at IMS Brogan, leading a team providing strategic advice to companies with new products

coming to market and ongoing consultation on the rules for existing drugs post launch. • Formerly Head of Price Regulation Consulting at Brogan Inc, Joan has supported many major pharmaceutical companies

with the preparation of pricing submissions to the Patented Medicine Prices Review Board (PMPRB), gaining extensiveinsights into the operation of the Canadian pharmaceutical market.

• Joan holds a Bachelor’s degree in Life Sciences from Queen’s University in Kingston, Canada, and an MBA from theUniversity of Ottawa, Canada.

Charles Makin, MS, MBA, MM, BS• Charles Makin is Principal RWE Solutions & HEOR at IMS Health, leading value strategy development, economic

evaluations and health outcomes research studies and providing direction on the use of observational research forevidence-based healthcare decisions. He has served as principal investigator on numerous US-based and global databaseanalyses, economic modeling, multi-country patient registries, adherence interventions, systematic literature reviews,value development plans and PRO research.

• A recognized leader and widely published author, Charles has deep insight into best practices in global research andmarket access. He was previously Global Head of Research Design and Proposal Development at UnitedHealth Groupwhere he directed and managed research design activities for all health outcomes, economics and drug safety research.He also worked as Research Operations Manager at WellPoint, leading project teams to execute HEOR projects.

• Charles holds a Bachelor’s degree in Pharmacy from the University of Pune, India, a Master’s degree in PharmacyAdministration from Purdue University, Indiana, and an MBA (summa cum laude) in Marketing Management and aMaster’s degree in Management (summa cum laude), both from Goldey Beacom College, Delaware.

Julie Munakata, MS• Julie Munakata is Principal RWE Solutions & HEOR at IMS Health, with a particular focus on global economic modeling,

value development planning, and survey data analysis.• An accomplished researcher and author of more than 25 original articles, Julie has extensive experience in managing

clinical trials, health economic studies and decision analytic modeling work, gained in senior roles at ValueMedicsResearch LLC, the VA Health Economics Resource Center and Stanford Center for Primary Care & Outcomes Research, andWyeth Pharmaceuticals.

• Julie holds an MS in Health Policy and Management from the Harvard School of Public Health and a BS in Psychobiologyfrom the University of California, Los Angeles.



Michael Nelson, PHARMD • Dr. Michael Nelson is Senior Principal RWE Solutions & HEOR at IMS Health, with particular expertise in retrospective

database research, prospective observational research, health program evaluation, and cost-effectiveness analysis.• During a career that includes leadership roles in HEOR at PharmaNet, i3 Innovus, SmithKline Beecham, and

DPS/UnitedHealth Group, Mike has gained extensive experience in health information-based product development,formulary design, drug use evaluation, and disease management program design and implementation.

• A thought leader in health economics for more than 20 years, Mike holds a doctorate in Pharmacy and a Bachelor ofScience degree, both from the University of Minnesota College of Pharmacy. He also served as an adjunct clinical facultymember at the University of Minnesota whilst in clinical pharmacy practice.


IMS HEALTH ECONOMICS & OUTCOMES RESEARCHPage 72 IMS HEALTH ECONOMICS & OUTCOMES RESEARCH

Núria Lara Surinach, MD, MSC• Dr. Núria Lara is Principal RWE Solutions & HEOR at IMS Health, with a particular focus on the design and coordination of

local and international observational and patient-reported outcomes studies.• A former practicing GP and clinical researcher, Núria’s experience spans roles in outcomes research at the Institute of

Public Health in Barcelona and in Catalan Health Authorities, and consulting positions within the pharmaceutical andmedical device industries focusing on medical regulatory and pricing affairs, pharmacoeconomics and market accessstrategies.

• Núria holds an MD (specializing in Family and Community Medicine in Barcelona), and a Master’s degree in Public Healthfrom the London School of Hygiene and Tropical Medicine and London School of Economics.

Vernon Schabert, PHD• Dr. Vernon Schabert is Senior Principal RWE Solutions & HEOR at IMS Health, with a particular focus on the assessment and

validation of patient-reported outcomes (PRO) instruments, retrospective analyses of claims and survey databases, andprimary data collection surveys.

• A founder and former President of Integral Health Decisions, Inc, Vernon has extensive experience in conducting claimsanalyses, creating custom administrative databases, developing business intelligence software, and leading nationalquality research projects, gained in roles with Thomson Reuters, Strategic Healthcare Programs LLC, and CIGNAHealthCare. His expertise spans numerous disease areas and diverse topics including medication adherence, inpatientsafety and outcomes in post-acute care.

• Vernon holds a PhD in Personality and Social Psychology from Stanford University and a BA in Psychology from PrincetonUniversity.

Jon Resnick, MBA• Jon Resnick is Vice President and General Manager RWE Solutions at IMS Health, leading the company’s global RWE &

HEOR business, including the development of RWE strategy, offerings, collaborations and foundational technologies tomeet the RWE needs of healthcare stakeholders.

• A former Legislative Research Assistant in Washington DC and member of the Professional Health and Social Security stafffor the US Senate Committee on Finance, Jon has 10 years consulting experience at IMS. He was most recently responsiblefor leading the European management consulting team and global HEOR business teams of 300 colleagues, advisingclients on a wide range of strategic, pricing and market access issues.

• Jon holds an MBA from the Kellogg School of Management, Northwestern University, with majors in Management andStrategy, Finance, Health Industry Management, and Biotechnology.

Amy O’Sullivan, PHD• Dr. Amy O’Sullivan is Principal RWE Solutions & HEOR at IMS Health, with a particular focus on global economic modeling

to support product reimbursement in jurisdictions around the world. • Highly experienced in the economic evaluation of medical technologies, Amy’s background includes roles at Policy

Analysis Inc. (PAI) and most recently in a senior capacity at OptumInsight (formerly i3 Innovus). She has led numerouspharmacoeconomic and outcomes research studies including cost-effectiveness analyses, budgetary impact analyses,burden-of-illness studies and piggyback economic evaluations. Her research spans a wide range of therapeutic areas,including autoimmune conditions, CV disease, CNS and behavioral health disorders, metabolic disorders, musculoskeletalconditions, oncology, respiratory disease and women’s health.

• Amy holds a PhD in Health Economics from the Johns Hopkins University Bloomberg School of Public Health, Baltimore,and a BA in Economics and English from Boston College.

Carme Piñol, MD, MSC• Dr. Carme Piñol is Principal RWE Solutions & HEOR at IMS Health, with more than 20 years experience in the

pharmaceutical industry spanning clinical research, health economics and market access. • Previously Head of Market Access for Spain at Bayer, a role that included pricing, HEOR, advocacy and institutional

relations with the Regions, Carme is a Board member of the Spanish Association of Health Economics as well as the ISPORSpain Regional Chapter and coordinator of the Pharmacoeconomics Interest Group of the Spanish Association of Medicineof the Pharmaceutical Industry (AMIFE). She has authored more than 60 communications in international and nationalcongresses and more than 20 papers in peer-reviewed journals.

• Carme holds an MD from the Autonomous University of Barcelona, an MSc in Pharmacoeconomics and Health Economicsfrom Pompeu Fabra University, an MSc in Health Research from Castilla-La Mancha University (UCLM), and an ExecutiveProgram Degree from ESADE Business School, Barcelona, Spain. She is currently completing a PhD in Health Research atUCLM.



AccessPoint - Issue 3 Page 73AccessPoint - Issue 3 Page 73

Arnaud Troubat, PHARMD, MBA, MHEM• Dr. Arnaud Troubat is Principal RWE Solutions & HEOR at IMS Health. He has extensive consulting experience and special

expertise in the development of registration dossiers and market access strategies across a large number of therapeuticareas.

• A pharmacist by training, Arnaud began his career at the French pharmaceutical industry association (LEEM), supportingmembers in understanding and interpreting the changing economical environment in France. He then spent a number ofyears in pharmaceutical affairs at ICI, leading regulatory work on registration submissions and reimbursement strategies,before subsequently moving into consulting. Most recently, he was Director at Carré-Castan Consultants, managing ateam working for a wide range of pharmaceutical companies.

• Arnaud holds a Doctor of Pharmacy degree and an MBA from IAE Paris and a Master’s degree in Health Economics andManagement from Paris-Dauphine University.

Dana Vigier, MD• Dr. Dana Vigier is Senior Principal RWE Solutions & HEOR at IMS Health, applying in-depth expertise and extensive

experience in pharmaceutical pricing, reimbursement and market access to help clients meet the growing challenges oftoday’s increasingly complex product launch process.

• A medical doctor and INSEAD executive, Dana’s background spans 15 years in pharmaceuticals and includes roles in R&D,commercial, market access, strategy and government affairs at GlaxoSmithKline, Organon and 3M Pharma. She hasworked on numerous pricing and reimbursement negotiations and designed and implemented national andinternational Phase II, III and IV studies across a wide range of therapy areas.

• Dana holds an MD from Bucharest Medical University, Romania and the Paris-Cochin Faculty, Paris, France.

Massoud Toussi, MD, MSC, PHD, MBA • Dr. Massoud Toussi is Principal and Medical Director RWE Solutions & HEOR at IMS Health with particular expertise in

methodological and operational aspects of clinical research to assure the quality of interventional, observational anddatabase studies.

• Previously Head of Global Clinical Research Operations at Cegedim, Massoud has also worked at the French HighAuthority for Health and various CROs as Project Lead, Scientific Manager and Operations Director. His experience includesdefining and elaborating a new service process in drug safety signal detection and transmission.

• Massoud holds an MD from Mashad University in Iran, an MSc in Medical Informatics and Communication Technologyfrom Paris IV, a PhD in Medical Informatics from Paris XIII University and a diploma in Transcultural Psychiatry from ParisNord University.

Rolin Wade, RPH, MS• Ron Wade is Principal, RWE Solutions & HEOR at IMS Health, and a recognized expert in the applications and limitations of

applying large retrospective datasets and late-phase datasets to health economics and outcomes research.

• Prior to joining IMS, Ron served as a Healthcare Executive and Principal Investigator with Cerner Research, directingretrospective database research using EMRs, administrative claims and other publically available databases. He waspreviously Research Director at HealthCore, where he led project teams in health outcomes research, economic modeling,safety/epidemiology and prospective observational research. He also has extensive experience generating evidence tosupport value messages to managed care, government payers and public health associations, gained in variousleadership roles within the pharmaceutical industry.

• A widely published author with expertise across a broad range of therapy areas, Ron is an invited lecturer at colleges ofpharmacy and he has served in leadership roles with the American College of Clinical Pharmacy and the Academy ofManaged Care Pharmacy. He is a licensed pharmacist and holds a BS in Pharmacy and an MS in Pharmaceutical Sciencesfrom the University of the Pacific, California.

Jovan Willford, MBA• Jovan Willford is Principal RWE Solutions at IMS Health, supporting growth strategy, offering development and

commercialization of RWE solutions. • Jovan’s background includes more than 10 years of strategic advisory experience across payers, providers, life science

organizations and technology companies, including several cross-industry collaborations to advance quality and value ofcare delivery.

• Jovan holds an MBA from the Kellogg School of Management, Northwestern University, with majors in Management andStrategy, Managerial Economics and International Business, and an undergraduate degree from the University of NotreDame with majors in Marketing and Philosophy.



About IMS Health

IMS Health is a leading worldwide provider of information, technology, and services dedicated to making healthcareperform better. With a global technology infrastructure and unique combination of real-world evidence, advancedanalytics and proprietary software platforms, IMS Health connects knowledge across all aspects of healthcare to helpclients improve patient outcomes and operate more efficiently. The company’s expert resources draw on data fromnearly 100,000 suppliers, and on insights from 39 billion healthcare transactions processed annually, to serve more than5,000 healthcare clients globally. Customers include pharmaceutical, medical device and consumer health manufacturersand distributors, providers, payers, government agencies, policymakers, researchers and the financial community.Additional information is available at www.imshealth.com

©2013 IMS Health Incorporated and its affiliates. All rights reserved. Trademarks are registered in the United States and in various other countries.

ACCESSPOINT0513

EUROPE210 Pentonville RoadLondon N1 9JYUnited KingdomTel: +44 (0) 20 3075 4800

[email protected]/rwe

THE AMERICAS1725 Duke StreetSuite 510Alexandria, VA 22314USATel: +1 (703) 837 5150

ASIA PACIFIC8 Cross Street #21-01/02/03PWC buildingSingapore 048424Tel: +65 6412 7365

LATIN AMERICAInsurgentes Sur # 2375 5th FloorCol. TizapanMéxico D.F. - C.P. 01090México Tel: +52 (55) 50 62 52 00and +1 917 542 5844

IMS REAL-WORLD EVIDENCE SOLUTIONS AND HEALTH ECONOMICS & OUTCOMES RESEARCHis based in 18 countries worldwide with regional headquarters in:

IMS AccessPoint 6 - May 2013

Documents

Transcript of IMS AccessPoint 6 - May 2013