CDISC Standards

Problems and Solutions: Some ExamplesPaul Terrill and Sarah Brittain

Aim

To discuss some problems met when creating and processing datasets that follow SDTM (and ADaM) standards.

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Introduction

MDSL InternationalSpecialist CRO Supporting Pharma/Biotech companies with limited in-house statistical and data management experience

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IntroductionOverall Problem

Clients have limited knowledge about CDISCNot involved from beginning

ConsequencesDifficult to retrospectively follow CDISC

SolutionsTake on decisions for clientsIncreased consultancy workBe involved from beginning!

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Protocol/CRF Design not CDISC

Tables and listings to follow protocol/CRFSDTM datasets requiredProblems:1. CRF not CDASH2. Trial Dataset Creation

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Original data (CRF) not collected according to CDASH / controlled terminology

SolutionMap CRF data to SDTM controlled terminologyBack code in ADaM for tables and listings

Problem 1: CRF not CDASH6

Problem 1: CRF not CDASHExample 1: Study Termination

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Study completed according to protocol?

If no, indicate one primary reason

- Lack of efficacy- Adverse event- Subject Request- Protocol Deviation- Lost to Follow-up- Death- Other

SDTM (DS domain) ADaM (ADDS)LACK OF EFFICACY LACK OF EFFICACY

ADVERSE EVENT ADVERSE EVENT

WITHDRAWAL BY SUBJECT SUBJECT REQUEST

PROTOCOL VIOLATION PROTOCOL DEVIATION

LOST TO FOLLOW-UP LOST TO FOLLOW-UP

DEATH DEATHOTHER OTHER

Problem 1: CRF not CDASHExample 2: Adverse Events

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Action taken (tick all that apply):

- None (1)

- Study Drug Discontinued (2)

- Study Drug Stop and Restart (3)

- Treatment (4)

SDTM ADaM (ADAE.AEACT)

(1) SUPPAE:QLABEL=‘Action Taken None’QVAL=‘NONE’

NONE

(2) AE.AEACN=‘DRUG WITHDRAWN’ STUDY DRUG DISCONTINUED

(3) AE.AEACN=‘DRUG INTERRUPTED’ STUDY DRUG STOP AND RESTART

(Not 2 or 3) AE.AEACN=‘DOSE NOT CHANGED’(4) AE.AECONTRT=‘Y’ TREATMENT

Problem 2: Trial Datasets

Trial datasets not thought about up front

Solutions:Retrospectively produce datasets (time

consuming and tricky)Recommend create trial design datasets whilst

developing protocols for future projects

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Problem 2: Trial DatasetsExample 1: Trial Arms

Study with open-label period followed by double-blind period (two treatments) followed by an optional extension study

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Problem 2: Trial DatasetsExample 1: Trial Arms

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Create 4 trial arms, although there are only 2 blinded treatments:1. Open-label – Treatment 12. Open-label – Treatment 1 – Extension study3. Open-label – Treatment 2 4. Open-label – Treatment 2 – Extension study

Problem 2: Trial DatasetsExample 1: Trial Arms

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ARMCD

ARM TAETORD

ETCD ELEMENT TABRANCH EPOCH

AB TRT1 1 SCREEN SCREENING SCREENING

AB TRT1 2 OLTRT OPEN LABEL TRT RANDOMISATION TO TRT1

OPEN-LABEL

AB TRT1 3 TRT1 TRT1 DOUBLE-BLIND

AB TRT1 4 PT POST-TREATMENT NOT ENTER EXTENSION

POST-TREATMENT

AB TRT1 5 FU FOLLOW UP FOLLOW UP

ABX TRT1-EXT 1 SCREEN SCREENING SCREENING

ABX TRT1-EXT 2 OLTRT OPEN LABEL TRT RANDOMISATION TO TRT1

OPEN-LABEL

ABX TRT1-EXT 3 TRT1 TRT1 DOUBLE-BLIND

ABX TRT1-EXT 4 PT POST-TREATMENT ENTER EXTENSION POST-TREATMENT

ABX TRT1-EXT 5 FUX FOLLOW UP EXTENSION

FOLLOW UP

AC TRT2 Etc Etc Etc Etc Etc

Problem 2: Trial DatasetsExample 2: Protocol with several ‘Parts’

Study split into different consecutive parts• Part 1: Three period crossover• Parts 2 and 3: Placebo controlled, single dose

based on dose selection from Part 1• Part 4: Three period crossover

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Problem 2: Trial DatasetsExample 2: Protocol with several ‘Parts’

Resulting trial design datasets largeUse of ARMCD and EPOCH to help distinguish between parts

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Problem 2: Trial DatasetsExample 2: Protocol with several ‘Parts’

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ARMCD ARM TAETORD

ETCD ELEMENT TABRANCH EPOCH

ABC ABC 1 SCREEN SCREENING RANDOMISATION TO TRT ABC

SCREENING

ABC ABC 2 A TRT A PART 1 FIRST TREATMENT EPOCH

ABC ABC 3 B TRT B PART 1 SECOND TREATMENT EPOCH

ABC ABC 4 C TRT C PART 1 THIRD TREATMENT EPOCH

ABC ABC 5 FU FOLLOW UP FOLLOW UP

BAC BAC 1 Etc Etc Etc Etc

B B 1 SCREEN SCREENING RANDOMISATION TO TRT B

SCREENING

B B 2 B TRT B PART 2 AND 3 TREATMENT EPOCH

B B 3 FU FOLLOW UP FOLLOW UP

A A 1 Etc Etc Etc Etc

Processing of Data

SDTM datasets repeatedly processedCreation of ADaMSome Listings

Efficient methods requiredProblem: 3. SUPPxx Domains

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Problem 3: SUPPxx Domains

Processing SUPPxx domainsSolutions:Use macros that combine SUPP datasets to

original domainCreate additional database where SUPP dataset

variables are included in parent domain (QNAM)

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Problem 3: SUPPxx Domains

RDOMAIN USUBJID IDVAR IDVARVAL QNAM QLABEL QVAL

SV 001 VISITNUM 3.1 SVUPREAS Primary Reason for Visit

REPEAT LAB TESTS

Example: Reason for unscheduled visit SUPPSV:

Unique Subject Identifier Visit Number Primary Reason for Visit

USUBJID VISITNUM SVUPREAS

001 3.1 REPEAT LAB TESTS

ADSV:

Non-Standard Data

Data collected / CRF does not fit into standard domainsSDTM still requiredCreate custom domain or...Put into QS (Questionnaire) type domain

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Problem 4: Non-Standard Data

Example: Subject Diary Drug Accountability

Primary source for drug accountability should be CRF but daily ‘Dose taken?’ Yes/No also collected on a subject diary

Solution:Put diary data into QS type domainUse this to derive compliance if necessary

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Problem 4: Non-Standard Data

Example: Subject Diary Drug Accountability

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QSSEQ QSTESTCD QSTEST QSCAT QSORRES VISITNUM QSDTC QSTPT1 TRT TRT TAKEN? DRUG YES 2 2010-08-01 DAY 1

2 TRT TRT TAKEN? DRUG YES 2 2010-08-02 DAY 2

3 TRT TRT TAKEN? DRUG NO 2 2010-08-03 DAY 3

4 TRT TRT TAKEN? DRUG YES 2 2010-08-04 DAY 4

5 TRT TRT TAKEN? DRUG YES 2 2010-08-05 DAY 5

Etc Etc Etc Etc Etc Etc Etc Etc

Development Program Consistency

Many studies form part of development program Consistency between studies requiredProblems: 5. TESTCD/PARAMCD6. Changing Standards

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Problem 5: TESTCD/PARAMCDConsistency of endpoints and associated xxTESTCD / PARAMCD across studies

Solutions:Create ongoing master test code list for each

program. Use csv format so it can be easily read in to create a format.

Try and use the same team within indications / development programs

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Problem 5: TESTCD/PARAMCD

Example: Test codes in a uterine myoma study

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TESTCD TESTM1VOL MYOMA 1 VOLUMEM1LOC MYOMA 1 LOCATIONM1TYP MYOMA 1 TYPEEtc EtcTMVOL TOTAL MYOMA VOLUMEULEN UTERUS VOLUMEUHGT UTERUS HEIGHTUDEP UTERUS DEPTH

UVOL UTERUS VOLUME

Problem 6: Changing StandardsChanging standards over long-running development programs

Solutions:Generally try to use most up to date standard,

but...Continually assess backwards compatibilityTry to keep the same team working on

development programs

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Questions26