Management OpenClinica® Infrastructure…..from scratch · – Original plan – In‐house DBMS...

Data Management and OpenClinica® Infrastructure…..from scratch

Elisa L Priest, MPHManager of Clinical TrialsBaylor Health Care System

Dallas, TexasMay 11, 2011

BackgroundBackground

I’ve given advice to small groups trying to develop g g p y g pan FDA compliant data management infrastructure. These groups are often looking at OpenClinica because it is open source and freelyOpenClinica because it is open source and freely available. However, OpenClinica is just one small part of the data management infrastructure that

d t b d l d Thi t tineeds to be developed. This presentation describes the process that Baylor Health Care System has gone through in the past 4 years to y g g p ydevelop an FDA compliant data management infrastructure to support investigator‐initiated trialstrials.

OverviewOverview

• Business NeedBusiness Need

• Document Requirements

l i i i f• Evaluate existing infrastructure

• Phase 1: Basic Infrastructure

• Phase 2: More Infrastructure

• Lessons Learned

BUSINESS NEEDBUSINESS NEED

Business NeedBusiness Need

• One small FDA regulated Phase II trial:– Original plan – In‐house DBMS– Would require extensive programming and validation to comply with

FDA regulations

Then…

• One larger FDA regulated, multi‐site Phase II Trial– Larger trial with similar startup timeframe– Multi‐site trial introduces complexity into data management

• One larger non‐FDA regulated, multi‐site Trial

Business NeedBusiness Need

to capture and manage clinical data into capture and manage clinical data in electronic format in a manner that

meets FDA requirementsmeets FDA requirements

DOCUMENT REQUIREMENTSDOCUMENT REQUIREMENTS

Document RequirementsDocument Requirements

• Regulatory (FDA) requirementsRegulatory (FDA) requirements– Investigator initiated (Investigator = Sponsor)

• Data Management requirements• Data Management requirements– Paper based Eventually EDC

– Multi‐site

– Multiple Trials

– Different phases and therapeutic areas

• Additional business requirements

Regulatory RequirementsRegulatory Requirements

• 21 CFR part 1121 CFR part 11

• Guidance for Industry: Computerized Systems Used in Clinical Investigations, May 2007g , y

• Guidance for Industry: Part 11, Electronic Records’ Electronic Signatures‐ Scope and Application, August g p pp g2003

• General Principles of Software Validation; Final Guidance for Industry and FDA Staff, January 2002

Data Management Process Requirements

• Creation of Entry Screens that mimic paper CRFy p p• Edit checks• Generate queries• Manage discrepancies (queries/SECs)• Study lock• Extract data• Coding dictionaries (MedDRA)E l d l di• External data loading

• Paper tracking/process tracking• Reporting• Reporting

EDC versus Paper RequirementsEDC versus Paper Requirements • EDC‐ Electronic Data Capture Trial

– Generally longer study start‐up• Paper‐based Trial

– All database building completed around the time of first patient in

– Sites may begin enrollment before db is fully developed

– Need data entry screens as the first sets of data come in (after

– Extensive edit checks built into forms (out of range, invalid, logic checks)

first sets of data come in (after CRFs are monitored against source)

– Edit checks on entry (usually too late for verifying source)

– Study Coordinators enter data at site

– Monitors compare source with l f

late for verifying source)

– Specialized data‐entry staff

electronic forms

– Need 10‐12 weeks from Final Protocol

– Monitors compare paper CRF with source

– Need 8‐12 weeks from final CRFNeed 8 12 weeks from final CRF

Additional Business RequirementsAdditional Business Requirements

• Training/Customer Support AvailableTraining/Customer Support Available

• IT support available

l i• External Hosting

EVALUATE EXISTINGEVALUATE EXISTING INFRASTRUCTURE

Existing InfrastructureExisting Infrastructure

• Human ResourcesHuman Resources– One Access database programmer

One data manager/ SAS programmer– One data manager/ SAS programmer

• Health Care System IT network

• SAS

• Microsoft Access

Existing InfrastructureExisting Infrastructure

Basically No data management infrastructureBasically…..No data management infrastructure

PHASE 1: BASIC INFRASTRUCTUREPHASE 1: BASIC INFRASTRUCTURE

Phase 1: Basic InfrastructurePhase 1: Basic Infrastructure

• Data management/Data Capture SoftwareData management/Data Capture Software

• Software Training

fi• Define Data Management Processes

• Develop SOPs

You should also have a Documented Plan!You should also have a Documented Plan!

Phase 1: SoftwarePhase 1: Software

Software DM ProcessesProcesses

Training Validation Training SOPS

Software Review ProcessSoftware Review Process

• Business needs/ RequirementsBusiness needs/ Requirements

• Determine priorities for software system:Determine priorities for software system:

– Time:When must you have the solution in place?y p

– Budget: What dollar figure must you not exceed?

– Value: To what extent does the product have to meet your needs?

– Scalability: how long do you expect to use this product?

Software InfrastructureSoftware Infrastructure

Review Choose Purchase

Installation ofInstallation of multiple

environments*ValidationTraining

Develop SAS b t tiabstraction

Validation PlanValidation Plan

• Review Validation Documentation Provided byReview Validation Documentation Provided by Akaza Research

• Develop User Requirements• Develop User Requirements

• Develop Performance Qualification Test Plan

• Develop or modify additional documentation

• Perform validation according to plan and documentdocument

• Review RegulationsJuly 2007

• Software Requirements andTimeline • Software Requirements and EvaluationFall 2007

•BIS software recommendations•PO for OpenClinica for BIIRSpring 2008

Timeline

•Installation of OpenClinica at BIIRFall 2008

• IHCRI requirements for OpenClinicaFall 2009

•RFP for OpenClinica and PO started•Data Management SOPsSpring 2010

•PO ApprovedSept 2010

•OpenClinica Installed•Validation Project BeganOctober 2010

•Site Audit of AkazaNovember 2010

•Finalization of Validation PlanDecember 2010

•Continue “moving towards compliance”May 2011

Phase 1: DM ProcessesPhase 1: DM Processes

Software DM ProcessesProcesses

Training Validation Training SOPS

Data Management Processes and h f hPhases of Research

Concept & Pl i E i Statistical&

DesignPlanning Execution Statistical

Analysis Reporting Termination

Exposure, outcomes, confounders identified

Data Management Plan (DMP)‐ Id data requirements and standards and processesData Management Plan (DMP)‐ Id data requirements and standards and processes

Preliminary tests of data collection tools/process

Finalize data collection tools edit checks DMP

Preliminary tests of data collection tools/process

Finalize data collection tools edit checks DMP

SAS or other programsSAS or other programs

Reports and publicationsReports and publications

Archive data

A hi

Archive data

A hiconfounders identified

Determine measurement

Statistical requirements for variables

requirements and standards and processes

Id all variables used in protocol and event table

Id all data from statistical analysis plan

requirements and standards and processes

Id all variables used in protocol and event table

Id all data from statistical analysis plan

Finalize data collection tools, edit checks, DMP

Build electronic capture (database, spreadsheet)

Validate e‐capture

Collect data

Finalize data collection tools, edit checks, DMP

Build electronic capture (database, spreadsheet)

Validate e‐capture

Collect data

Edit checks/cleaning

Analysis documentation

E ti t f ff t


Analysis documentation

E ti t f ff t

Estimates of effectEstimates of effect

Archive documents

Metadata/docs

Make data il bl f

Archive documents

Metadata/docs

Make data il bl fId all data for required reports: DSMB

Id all external data sources (charts, lab measures, ect)

Document variable formats

Id all data for required reports: DSMB

Id all external data sources (charts, lab measures, ect)

Document variable formats

Data receipt/tracking

Data entry/ verification

Queries and corrections

Data receipt/tracking

Data entry/ verification

Queries and corrections

Estimates of effectEstimates of effect available for re‐useavailable for re‐use

Id edit checks for variables

Data collection tools: paper Case Report Forms (CRFs)

Id edit checks for variables

Data collection tools: paper Case Report Forms (CRFs)


Coding

Data transfers from/to

Data integration


Coding

Data transfers from/to

Data integrationAnnotated CRFAnnotated CRF Data integration

Data process reports

SAS or other programs

Data integration

Data process reports

SAS or other programs

Developing Data Management Processes

Draft SOP ?

OC Training

Develop OC Expertise

+ Create DM Refine

creation?

OC Training + Use previous Experience

Processes Processes

StandardizeStandardize Processes

across studiesSOP creationDM Process

Training


• May be different for EDC vs PaperMay be different for EDC vs. Paper– May need SOPs for both

• Start as simple as possible and then add• Start as simple as possible and then add processes as need

• Does not need to be perfect at first• Does not need to be perfect at first

• Depending on software capabilities, may need additional infrastructure for tracking formsadditional infrastructure for tracking forms, queries…

Access based study database for tracking data– Access based study database for tracking data management processes including forms and queries


• Our approach:Our approach:– Develop processes and familiarity with OpenClinica with a paper‐based processOpenClinica with a paper based process

– Test EDC capabilities in a subset of patients to ensure clinical site comfortensure clinical site comfort

– Move to full EDC in future trials after infrastructure developmentp

Paper Paper + EDCPaper EDC EDC

EDC first‐‐Why not?EDC first Why not?

• Infrastructure for EDCInfrastructure for EDC– Knowledge/Expertise of OpenClinica

Provide Training for Clinical Site– Provide Training for Clinical Site• General data entry training

• Study‐Specific data entry trainingStudy Specific data entry training

– Provide Support for Clinical Site• Answer questions when neededAnswer questions when needed

– IT infrastructure for site

– Maintenance of security/permissionsMaintenance of security/permissions documentation

EDC first—Why not?EDC first Why not?

• Infrastructure for EDCInfrastructure for EDC– Edit Check programming

Additional validation– Additional validation

– User acceptance testing completed by clinical site

CRF d f d ti ( d lid t d) b– eCRFs ready for production (and validated) by time of study start

Why SOPs? Quality: GCP 5.1Why SOPs? Quality: GCP 5.1

• The sponsor is responsible for implementing and maintaining quality assurance and quality control g q y q ysystems with written SOPs to ensure that trials are conducted and data are generated , d t d ( d d) d t d idocumented (recorded), and reported in compliance with the protocol, GCP, and the applicable regulatory requirements.applicable regulatory requirements.

ICH Guideline for Good Clinical Practice E6(R1) 1996

Data Management SOPsData Management SOPs

• SOPs on DM Processes: all phases of researchSOPs on DM Processes: all phases of research– Study Planning/Start up

Execution– Execution• Filing/Storage

• Paper work flowPaper work flow

• Paper tracking

• Discrepancies/Queries

• Data Extract

– Study closure/ Archiving


• Create Listing of all SOPs neededCreate Listing of all SOPs needed– caBIG– Good Clinical Data Management Practices from gthe Society for Clinical Data Management

– Practical Guide to Clinical Data Management (Prokscha)

• Prioritize Listing• Create SOPs• Responsibilities for personnel


• 25 Priority 1 SOPs25 Priority 1 SOPs

• Around 400 hours of creation time

SOPs– SOPs (versioned)

– Procedure descriptions/work instructions (not versioned)

– Template Forms

SOPsSOPs

SchedulingScheduling

SchedulingScheduling

• Estimated Effort vs. duration– DraftCommittee Review– Committee Review

– Editing/Final Committee Review and sign off– Executive Review and sign off

Example Weekly Schedule and TasksExample Weekly Schedule and Tasks

• This week 05/17/2010

• Writing: Due to Executive on 06/04/2010– Study Database Data editing– Data Cleaning and Review

• Reviewing: Due to Review this week. Due to Executive on 5/28/10– Database design and Creation (Change name to Study Database

Design and Creation)Design and Creation)– Study Database Validation– Study Database Edit Check Programming

• Final Editing Monday/Tuesday/Wednesday: Due to ExecutiveFinal Editing Monday/Tuesday/Wednesday: Due to Executive 05/21/2010– CRF Forms and Flow Management– Data Management Roles and Responsibilitiesg p

SOP on ProgrammingSOP on Programming

Phase 1: Basic InfrastructurePhase 1: Basic Infrastructure

• Maturity of TrainingMaturity of Training

d “Informal Training” of Baylor‐specific h Develop ‘customized’1 Person Trained Informal Training of others as needed

Baylor specific standardizedtraining Train the Trainer Develop customized

training for each study

• Training Topics– Data entrya a e y

– Study Setup

– Data PrinciplesData Principles

– CRF Creation

PHASE 2: MORE INFRASTRUCTUREPHASE 2: MORE INFRASTRUCTURE

Phase 2: More InfrastructurePhase 2: More Infrastructure

• Infrastructure Strategic PlanningInfrastructure Strategic Planning

• EDC Support

SO i i 2/3• Data Management SOPs: Priority 2/3

• Data Management Training

• Data Management Competencies– Job DescriptionsJob esc p o s

– Training Matrix

– Personnel Development PlansPersonnel Development Plans

Lessons Learned: Implementing Clinical Trials in OpenClinica®


Dallas, TexasMay 11, 2011

Lessons Learned: Implementing l l l lClinical Trials in OpenClinica

I will review my experience buildingI will review my experience building infrastructure using OpenClinica. Specifically, lessons learned while implementing multiplelessons learned while implementing multiple investigator initiated trials in OpenClinica. This will include organizing and tracking eCRFwill include organizing and tracking eCRFdevelopment, versioning eCRFs, and creating paper CRFs compatible within the OpenClinicapaper CRFs compatible within the OpenClinicaframework

Lessons Learned: OverallLessons Learned: Overall • Organization

• Documentation

• Change is certain!!g

Paper CRFsPaper CRFs

• Read the protocolRead the protocol

• Create your own study event chart

C h id d d h (if• Compare the provided study event chart (if there is one) with yours to identify di idiscrepancies

• Identify/Organize data into repeating modules

• Identify the eCRF sections and headersIdentify the eCRF, sections, and headers

Study Event Chart/ CRF MatrixStudy Event Chart/ CRF Matrix


• Recreate paper CRFs to be more compatible with OC interface– Listen to your gut: reformat if necessary!

• Be consistent in formatting to represent– eCRF titleS i– Section

– Headers

• Limit the number of columns• Limit unnecessary repeating groups (for data extract)B i t t i h i (1 2 )• Be consistent in answer choices (1‐yes, 2‐no)

Formatting of eCRF and SectionsFormatting of eCRF and Sections


• Require testing of pCRFs with hardcopy proofRequire testing of pCRFs with hardcopy proof that they have been tested

• Send only PDFs to review• Send only PDFs to review

• Use a draft watermark and only remove it CRF donce CRFs are approved

• Versioning and tracking changes is critical

Organizing and Tracking eCRFsOrganizing and Tracking eCRFs

• Use study event chart formatUse study event chart format

• Track initial design, testing, versioning

k i d i• Track response options and response options text

• Be consistent on response options and text across eCRFs in a study

Tracking developmentTracking development

R O tiResponse Options

eCRF developmenteCRF development

• Be organized and consistentBe organized and consistent– Naming folders and files

Naming variables across eCRFs– Naming variables across eCRFs

– Response Options and text

eCRF developmenteCRF development

• Develop in stages– Local environment: Development and 1st testing– Review and approval by second person– Upload into testing environmentUpload into testing environment– Testing with real data by at least 2 different staff– Versioned and uploaded into production

• Cannot change variable labels types response optionsCannot change variable labels, types, response options rename variable

• Versioning/change control is time consuming

pCRFs and eCRFs versioningpCRFs and eCRFs versioning

• Link paper CRF versions with eCRF versionsLink paper CRF versions with eCRF versions.

Lessons LearnedLessons Learned

• You will make mistakesou a e sta es

• Learn from your mistakesLearn from your mistakes

• Use the communityUse the community– Prevent mistakes– Help others prevent mistakesp p– Contribute to Mantis when you find a potential problem

Thanks!Thanks!

• Dr. Sunni Barnes • Consultants– Suzanne Prokscha

• IHCRI Clinical Trials Team

– Dr. David Hardison

• Akaza ResearchIHCRI Clinical Trials Team– Monica Anand– Tyson Bain– Candice Berryman

• Akaza Research

y– Kristen Rose– Deepa Putuvakkat– Alicia Turoff

Thank you!Thank you!


Dallas, [email protected]

Management OpenClinica® Infrastructure…..from scratch · – Original plan – In‐house DBMS...

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