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Trial Design in the CDISC World

Trial Design in the CDISC World. Albert Chau 26 July 2011. Agenda. How to construct Trial Design datasets Relationships with other SDTM domains Challenges for new users Confusion of definitions/terms Granularity Case study in oncology. Trial Design Domains.

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Trial Design in the CDISC World

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  1. Trial Design in the CDISC World Albert Chau 26 July 2011

  2. Agenda • How to construct Trial Design datasets • Relationships with other SDTM domains • Challenges for new users • Confusion of definitions/terms • Granularity • Case study in oncology

  3. Trial Design Domains • Information about study design • No subject data • Describe the overall trial design and plan via data representation

  4. Trial Design Datasets • Trial Arms (TA) • Trial Elements (TE) • Trial Visits (TV) • Trial Inclusion /Exclusion (TI) • Trial Summary (TS) • Start thinking about this before you start the other SDTM datasets!

  5. Trial Summary (TS) Dataset • Summary of trial information • No link to subject-level data in SDTM • Common questions: • What need to be included? • Why are we generating this?

  6. Trial Inclusion/Exclusion (TI) • Not subject-oriented • Link to IE domain • STUDYID, IECAT, IETESTCD, IETEST • Best to create TI first, before you tackle IE • Common questions: • How to truncate if >200 characters? • Protocol amendment: do we need to add to TI only the changed criteria or all criteria? • Local amendment

  7. TA / TE / TV datasets • A data representation on the different epochs, arms and visit structure in the study • Where to start? • Is there a systematic approach?

  8. Example 1 – Trial Design Schema Drug A Follow-up Screen Drug B Follow-up

  9. Epoch Drug A Follow-up Screen Drug B Follow-up EPOCH Screening Follow-up Treatment

  10. Arm / Treatment Strategy Drug A Follow-up ARM(Treatment Strategy) Screen Drug B Follow-up Screening Follow-up Treatment 1 2

  11. Arm / Treatment Strategy Drug A Follow-up Screen Study Cell Drug B Follow-up Screening Follow-up Treatment 1 Screen Drug A Follow-up 2 Screen Drug B Follow-up

  12. Trial Design Matrix Screening Follow-up Treatment A Screen Drug A Follow-up B Screen Drug B Follow-up

  13. TE (Trial Elements) • What are the elements? • Unique study cell values (=ELEMENT) Screen Drug A Drug B Follow-up

  14. TE (Trial Elements) • Assign an element code (ETCD) to each value, define the start of each element (TESTRL) and end of each element (TEENRL or TEDUR) ETCD ELEMENT TESTRL Screen SCRN Informed Consent Drug A First dose of drug A A Drug B B First dose of drug B Follow-up 1 week after last dose of drug FU

  15. Trial Arms (TA) Dataset • Go back to the Trial Design Matrix • 1 study cell = 1 row of record in TA • So in our example we expect 6 rows of record • ARM / ARMCD • = Treatment Strategy • Not necessarily the same as the actual drug names/codes • ETCD / ELEMENT • Must match up with the values in TE

  16. TE -> SE (Subject Elements) • Shows the trial progress of each subject • Whether a subject passes through each element • Timing of each element

  17. Trial Visit (TV) Dataset • Describe the planned visits in a trial • VISITNUM and TRSTRL is required • ARMCD expected • VISIT and VISITDY permissible • 1 record per planned visit per arm • A “visit” may span over several days (eg screening visit)

  18. TV -> SV (Subject Visits) • Shows the actual visits of each subject • Compare against the scheduled/planned visits or assessments in TV • Include unscheduled visits • Designation of VISITNUM becomes crucial • Whole number for planned visits • Decimals for unscheduled visits in SV – and slot into right place

  19. Challenges in 1 oncology study • Leukemia • Drug X (Days 1-4) is the current standard of treatment • Drug A (Days 1-7) is the experimental treatment • Patients to receive: • Drug A+X vs Drug X (1 course of treatment) • If patients on drug X not responding, then option to “crossover” to drug A+X (1 further course) • Follow-up • Responders: Efficacy follow-up (+ post-remission therapies where applicable) • Non-responders and relapse: Survival follow-up

  20. Study schema Drug A + X Efficacy FU (q1 months) Screen Drug X SurvivalFU (q3 months) Drug A + X

  21. Question 1 – what are the epochs? • 1 Treatment epoch or separate into 2? • 1 Follow-up epoch or separate into 2? Drug A + X Efficacy FU (q1 months) Screen Drug X SurvivalFU (q3 months) Drug A + X Treatment Efficacy FU Survival FU Screening

  22. Question 2 – How many arms? • For the patients on Drug X and then rollover to Drug A+X – should this considered as a separate “arm”? Drug A + X Efficacy FU (q1 months) Screen Drug X SurvivalFU (q3 months) Drug A + X

  23. Question 3 – Granularity of Elements • Do we model using “Treatment”+”Rest” or simply “Treatment” (which includes rest period)? • Length of “Rest” differs between patients • Do we need to distinguish between “Treatment” and “Rest”? Treatment (Day 1 - 7) Rest (Day 8 – ??) Treatment (Day 1 - 37)

  24. Question 4 – Describing Trial Visits • How to number the visits, when you don’t know how many visits there are up-front? • Don’t have to be consecutive numbers • Example: • 1st course of treatment: Start with VISITNUM=11 • Cross-over: Start with VISITNUM=51 • Efficacy follow-up: Start with VISITNUM=201 • Survival follow-up: Start with VISITNUM=501

  25. Question 5 – Varying Trial Visits • During Efficacy Follow-up, patients can receive “post-remission therapies”. • “Reset” follow-up clock from post-remission therapies • How to model Trial Elements and Visits?

  26. Question 5 – Varying Trial Visits • Suggestion: • Start with VISITNUM=201 for Efficacy Follow-up • Trial Element: Up to the next post-remission therapy • 1st Post-Remission therapy: VISITNUM=250 • 2nd Post-Remission therapy: VISITNUM=300 • etc

  27. Question 6 – Post-remission therapies • For post-remission therapies in efficacy follow-up, the choice is down to the treating physician • Can potentially be Drug X or any other therapies • Should we create Trial Elements for the different therapies?

  28. Question 7 – Randomised but not treated • Randomisation usually starts 1-2 days before start of treatment due to logistic reason • What is the start and end of “Screen” and “Drug A”/”Drug A+X” trial elements in TE? • How to capture these patients in SE? • Should randomisation be a separate visit in TV/SV?

  29. Question 8 – When is a visit no longer “planned” • Planned visits for lab assessments: Day 15, Day 21 • A patient had lab taken on Day 17 and Day 22 instead • Should these be put into planned visits of Day 15 and Day 21, or unscheduled visits?

  30. Other challenges in oncology studies • Post-remission therapy will be given and patients will be followed up “according to institution’s standard treatment practice” • Dose escalation studies – how many arms? • Legacy studies: Do we need to provide trial design datasets?

  31. Summary • Construction of TA/TE/TV • Study Schema  Epoch  Arm  Study Cells • Unique study cells = rows in TE • All study cells = rows in TA • If all arms have same visits, then 1 set of visits for all arms. Otherwise 1 set of visits for each arm. • Complex study designs • Systematic approach will make life easier • Think at protocol/CRF design stage – don’t wait till the end • Details vs ease of use

  32. Thank you! E-mail: albert@datacision.co.uk Tel: +44 (0)7904 106966 Web: www.datacision.co.uk

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