The application of STDM in a no-profit and disease specific organisation - CDISC Italian User Group,...

Early Drug Development Early Drug Development In OncologyIn Oncology

CDISC Italian-Speaking User Group Meeting 2008Milan, 16th May 2008

The Application of SDTM in a no-profit and disease specific organization

Angelo TinazziMedical Informatics and Biometry UnitSENDO Tech S.r.l. – Milan; ITALY

2CDISC Italian-Speaking Usergroup Meeting 2008 – Angelo Tinazzi (SENDO Tech S.r.l.)The Application of SDTM in a no-profit and disease specific organisation

SENDO Background• No profit organisation acting as a CRO• Disease Oriented (Oncology)• Early Drug Development

– Phase I (in humans) for new compounds and for new combinations (Ib-sponsored or investigator initiated), Phase II (single indication e.g. Advanced Breast Ca.)

– No involvement in regulatory data submission

?? So why bother about (CDISC) standards ??

A


SDTM Pitfalls at SENDO • Oncology heterogeneity• New compounds, New Schema à unknown

mechanism• Phase I in patients• Customer-oriented

– Internal (Strong Principal-Investigator leadership)– External (Sponsor)

However some ‘standardisation’ can be applied

A


SENDO Customers• A multinational company imposed its

standards• A big national company imposed its ‘recent’

(<5 years) standards• Other companies/biotechs accept SENDO

‘rules’– Some are starting asking “is your database CDISC

compliant?” …. Is this an appropriate question?• Common terminology not yet applied

– e.g. several ways for defining AE drug relationship

A


Existing standards and projectsin OncologyStandard Cancer Terminology• Tumor-extension classification (e.g. TNM)• Tumor response evaluation (e.g. RECIST

Criteria)• NCI-AE Common Toxicity Criteria

NCI and CDISC collaboration for ControlledTerminology CDISC Initiative

• CDISC Review• Enterprise Vocabulary Services (EVS)• Cancer Data Standards (caDSR)

A


SENDO SDTM ImplementationBack to Origin (2003)Existing standards• By module (two digits abbreviation)• Variable naming convention

– Module abbreviation + Variable abbreviation + Variable Typei.e. C for Character, N for Numeric, D for Datee.g. AEEVEC for Adverse Events Verbatim

• Standard (SI Laboratory Standard Conversion), Therapeutic (Oncology) and Company standard terminology

• Tools for Clinical Data Review

A


References used in this presentation• CDISC SDTM Implementation Guide (IG) –

Version 3.1.1• Study Data Tabulation Model (MOD) - Version

1.1• All roads lead to SDTM? Which one shall we

take? PhUSE 2007, Paper RA08 (PHUSE)• Creation of Submission and Analysis Datasets

by following SDTM and ADAM requirements. PhUSE 2007, Paper RA05 (PHUSE)


SDTM Implementation – when toimplement?

• Build SDTM entirely in the CDMS (pre)• Build SDTM entirely on the ‘back-end’ (post)• Build SDTM using a Hybrid approach (hybrid)

CDMS=Clinical Data Management System


SDTM ImplementationOther points to consider• CDMS metadata libraries support• CDMS flexibility in clinical database updates• CDMS flexibility in performing complex derivations• Your Data-Management team ‘language’• Biostat preferences• Existing data-management and analysis standard tools

The decision should be driven by the existing company standards regulatingthe entire clinical trial process from data collection to data analysis

& PHUSEA


SDTM Implementation – SENDO approach (1)Hybrid Model• CDMS Restrictions• About 70% of SDTM applied in the CDMS• CDMS data-collection oriented

CDMS “source data”SDTM-like

SAS Programming

Oracle (CT/OC)Other Prop. Db

SAS

SENDO-SDTM CDISC-SDTM

Full CDISC-SDTM + Sponsor Requirements

SENDO-ADAM Standard SAP/TLG

Listings Pt Profiles Other tools

Std Med Review Tools


SDTM Implementation – SENDO approach (2)Apply a Hybrid Model (SDTM-SENDO)In the CDMS• SDTM tables (standard domains) naming• SDTM variables naming (including derived

variables derived in SAS)• Simple derivations• If a sponsor plans to submit data to FDA, apply

complete post-processing in SAS

A


SDTM Implementation – SENDO approach (3)SAS Post-processing Mapping (1)

• Direct Mapping• Complete variables mapping (Rename)

e.g. STDTCàAESTDTC, STDTCàEXSTDTC• Complete derivations• Combine multiple CDMS variables

e.g. datepart variables

& MOD Sect 2.2.5 [Timing Variables] Start Date/Time of an observation

A


SDTM Implementation – SENDO approach (4)SAS Post-processing Mapping (2)

• Combine multiple CDMS datasets– Horizontal to Vertical when required

e.g. vital signs

– Match ‘temporary’ non-standard modulese.g. chemistry + hematologyàLB

• Standardize format values• Apply new Formats

e.g. ISO date format• Creation/Update of Trial Design Datasets

MOD Sect 7.1 [Trial Design Datasets]Description of key aspects of the plannedconduct of a clinical trial (e.g. arms)

A


SDTM Implementation – SENDO approach (5)Cons of Hybrid Model

• Additional transformations are required thereforeyou need:– Additional Specifications– Additional Programming Activities– Additional Validation

• The data-management staff still speaking an otherlanguage (is it a pro or a con?)

• Possible delays in delivering SDTM (and ADAM)

A


SENDO map to SDTM – Issues (1)• CDMS modules (CRF section) that map to one

corresponing SDTM domain• e.g. Laboratory and Normal Ranges

• SDTM Variables contain decoded text ratherthan code (and format/codelist to be applied)

• Other….specifyà In SUPPQUAL

& MOD sect 4.4Non-standard variables not presently includedin the general-observation-class models.

A


SENDO map to SDTM – Issues (2)• Sometimes it can be considered not analysis-

oriented…but this is sometimes due toprogrammers attitude• e.g. vertical vs horizontal structure

• Unclear where endpoints such death and survival should be collected• DS?• ad-hoc module? Findings or Events Class?

A


SENDO map to SDTM – Deviations (1)• Map new variables rather than using SUPPQUAL

• --DY variable = 0

• Common terminology not fully-integrated

• Use of code rather than decode

& MOD sect 2.2.5 [Timing Variables]Actual study day of Visit/Collection/Examin integer days& IG sect 4.1.4.4There is no study day 0

& MOD sect 4.4Non-standard variables not presently includedin the general-observation-class models.

A


SENDO map to SDTM – Deviations (2)• AESEV instead of AETOXGR• LBCTC (new variables) instead of

LBTOX/LBTOXGR• Assigned dose-levels in Trial Arms table

& MOD sect 3.2.2Planned Arms in the trial

A


SENDO map to SDTM Module-specific issues (AE)• Change in AE-condition in the same visit

– e.g. severity grade (IG 3.1.2–Findings CF module to be applied)• Multiple Action taken and Drug Relationship for drug-

combination trials• “Any AE occured” is not allowed

– & IG “When adverse events are collected with the recording of free text, some sponsors may enter a record into the data management system to indicate 'no adverse events,' for a specificsubject. For these subjects, do not include a record to indicate thatthere were no events unless AEOCCUR is being used to representrecords for non-occurring events. Otherwise, records should beincluded in the submission dataset only for adverse events thathave actually occurred”!!! AEOCCUR removed from IG 3.1.2 !!!

A


SENDO map to SDTM Module-specific issues (EX)Reasons for treatment change collected in ‘SUPPQUAL’(EX.EXADJ not applicable for multiple occurences)

SUPPEX (or EM) dataset

1Treatment DelayedDELAY2EXSEQEX01002S075MY01

RED

QNAM

1Dose Reduced2EXSEQEX01002S075MY01

QVALQLABELIDVARVALIDVARRDOMAINUSUBJIDSTUDYID

01 02 200850 5

51 5

x

x

EX

SUPPEX

Interventions (EX) dataset

2008-02-01DRUG122EX01002S075MY01

EXSTDTCEXTRTEXSEQVISITNUMDOMAINUSUBJIDSTUDYID

& IG sect 6.1.2The Exposure domain model records the details of a subject’s exposure to protocol-specified study treatment.


Creating a NEW Domain (1)• Check that none of the existing published

domains will fit the need• Use existing models as prototype or choose the

general observation class….How to determine where data belong in the

SDTM, & IG Page 129• Assign a 2–letter domain code (suggested X_?)

& IG Page 13

A


Creating a NEW Domain (2) Select the SDTM General Observation ClassesUnderstand the class your domain belongs to• InterventionsTreatments or Procedures intentionally administered either specified bystudy protocol or other treatments(Investigational Treatments, Conc Medications)

• EventsOccurences or incidents indipendent of planned study evaluationoccuring before or during the trial(Adverse Events, Medical History)

• FindingsPlanned evaluations to address specific questions(Physical Evaluation, Laboratory)

Change your (my) mind. Rather than Analysis oriented:

• Patient Characteristics• Treatment Exposure• Efficacy• Safety

….‘data-collection’ oriented

B


Creating a new Domain (3)SENDO-SDTM new modules• Prior Tumor History

– Prior Systemic Therapies (ST)– Prior Radiotherapies (PR)

• Tumor Response Evaluation (TAàCA)• Follow-up (FP)• Ad-hoc study modules (i.e. lymphoma-non-

hodgkin assessment ad-hoc measurements)

A


Creating a NEW Domain (4)• Add variables as follows (& IG sections)

• Use existing models as prototype or choose the general observation class


Creating a NEW Domain (5)Assign a variable name (& IG Section 10.4)


Creating a new Domain (6)Example: Tumor Assessment (CA)

• Periodic (every 2 months/visits) review of tumor lesions dimension• Measurement of Tumor-Lesions

– Classified as Target or Non-Target or New– Description– Organ location– Assessment date– Method used– Measure (mm)

IV SEGMENT 0 5 0 5 10 20070 1

2 0

2 20 1

0 5 10 2007PANCREATICNODULE

98


Creating a new Domain (7)Example: Tumor Assessment (CA)

IV SEGMENT 5 0 5 10 20071

2 0

2 21

0 5 10 2007PANCREATICNODULE

98

MM2215PANCREATIC NODULETARGET

MM

CAORRESU

2015IV SEGMENTTARGET

CAORRESCAMETHODCASCATCATESTCACAT

Variables, text or number, that describe the results or additional traits of the observation (e.g. units)

1.022CA01002S075MY01

1.011CA01002S075MY01

CASPIDCASEQDOMAINUSUBJIDSTUDYID

Variables which identify the study, the subject, the domain and the seq nr. of recordVariables which identify the study, the subject, the domain and the seq nr. of record

Variables which describe the timing of anobservation (e.g. date collection)

2007-10-051BASELINE

2007-10-051BASELINE

CADTCVISITNUMVISIT

--SPIDOptional Sponsor-defined referencenumber. Perhaps pre-printed on the CRF as an explicit line identifier on a Concomitant Medications page.

--CATUsed to define a category of relatedrecords. Examples: HEMATOLOGY, CHEMISTRY

--TESTVerbatim name, corresponding to the topic variable, the test or examinationused to obtain the measurement or finding. Examples: Platelet Count, Systolic Blood Pressure, etc.

--SCATUsed to define a further categorizationlevel for a group of related records.

Example: DIFFERENTIAL.

--METHODMethod of the test or examination


SAS Tools supporting SDTM - Proc CDISC (1)

• SDTM 3.1 data validation available in SAS 9.1.3– Service pack 4 available for download at

ftp.sas.com/techsup/download/hotfix/hotfix.html– 15 SDTM modules supported– Various checks on models / variables attributes

(e.g. variables not defined in the domain)

– Various checks on models / variables contents(e.g. missing values in required variables)

ftp://ftp.sas.com/techsup/download/hotfix/hotfix.html


SAS Tools supporting SDTM - Proc CDISC (2)Example validating DM dataset


SAS Tools supporting SDTM - Proc CDISC (3)Example validating DM dataset

Expected variable warningVariable AGEU is expected in this domain(DM), but is not present

Permissable variable noteVariable DMDY is permitted in this domain (DM), but is not present

Data Error Incorrect ISO time-unitsVariable REFNDTC has incorrect content in observation 4Incorrect data is 10-12-2008A time or date component was outside expected range

C


(SENDO) SAS tools created• Macro routines library

– Automatic Creation of dataset structures frommetadata definition files (matrix datasets)

– Standard derivation (i.e. date in ISO format)– Standard terminology application (i.e. Laboratory

Conversion, CTCAE common toxicity criteria)– Other complex derivations


Future improvements at SENDO (1)• Review current terminology• Full implementation of other SDTM elements to

automatize data-quality/review processes– Trial Design Datasets– Relationship (e.g. AEàCM for linking action taken

with corresponding concomitant medications)• Try to armonize PK and other data from external

providers• Complete ADAM implementation

A


Future improvements at SENDO (2)Adopt 3.1.2 improvemente.g. Protocol Deviations (Section 6.2.4.2 IG 3.1.2)The intent of the DV domain model is to capture protocol deviations thatoccured during the course of the study (other than inclusion/exclusion criteria) as per ICH E3 Section 10.2

CYLE01

EPOCH DVSTDTC

MINORNO BLOOD SAMPLE AT DAY 8

1DV01002S075MY01

DVDECODDVTERMDVSEQDOMAINUSUBJIDSTUDYID


Conclusion - SDTM (CDISC) Criticism?• Still SAS v5 transport dataset dependence

– Variable naming (8) limitation– Character Variable length (200) limitation

• Use of domain abbreviation in variable name• SDTM is not always suitable for data-entry and

data-validation

A


Conclusion - SDTM (CDISC) Alerts?• Applying SDTM is not just a variable renaming

activity• Hybrid model is a good compromise• Keep up-to-dated. If possible be involved in draft

documents review• Training• Find the answers using the CDISC public forum

www.cdisc.org/discussions/index.html

AB

http://www.cdisc.org/discussions/index.html


Conclusion - SDTM (CDISC) Requests?• A model for external peer reviewed data

e.g. Study endpoints such as tumor response


Conclusion - SDTM (CDISC) Benefits?• Standardisation improves data-quality• Tools Re-usability

e.g. data-listings and patient profiles for clinicaldata review

• Standardisation is possible even in a non-commercial (no-profit) organisation

• Academic Organisations should consider it forbetter data(knowledge)-sharing

e.g. Meta-analysis

A


Thanks and QuestionsMany thanks to all SENDO MIBU Staff• Alessandro Cattaneo• Anna Compagnoni• Enrica Paschetto• Sonia Colombini

Angelo Tinazzi, SENDO Tech S.r.l.Medical Informatics and Biometry UnitVia Visconti di Modrone 1220122 [email protected]

mailto:[email protected]

The application of STDM in a no-profit and disease specific organisation - CDISC Italian User Group,...

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