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Trials and Tribulations of SDTM Trial Design Mary Lenzen Octagon Research Solutions

Trials and Tribulations of SDTM Trial Design Mary Lenzen Octagon Research Solutions. ESUG Telecon July 26, 2011. Why Do Trial Design. Rapidly understanding the design of the study Standard and relatively simple data structures Relatively small number of rows of data and easy to comprehend

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Trials and Tribulations of SDTM Trial Design Mary Lenzen Octagon Research Solutions

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  1. Trials and Tribulationsof SDTM Trial DesignMary Lenzen Octagon Research Solutions ESUG Telecon July 26, 2011

  2. Why Do Trial Design • Rapidly understanding the design of the study • Standard and relatively simple data structures • Relatively small number of rows of data and easy to comprehend • Useful for both FDA reviewers and internal sponsor use • Information can be centrally accessible and searchable

  3. Trial Design: Overview Ordered by Complexity • Trial Inclusion/Exclusion Criteria (Lookup table) • Trial Summary (Descriptive attributes of trial) • Trial Visits (Planned) • Trial Arms (Planned) • Trial Elements (Planned)

  4. Creating Trial Inclusion/Exclusion • Challenge of 200 character limit • Truncation – potential for duplicate IETEST values • Need protocol or CRF for complete IETEST values • Subject IETEST/IETESTDC must match Trial Inclusion/Exclusion IETEST/IETESTCD

  5. Creating Trial Summary • Key details about the trial • One record per parameter value • TSGRPID used to group multiple related parameters such as Dose, Units, Frequency etc • TSSEQ used as a key for multiple records with the same parameters

  6. Creating Trial Visits • Planned schedule of Visits • Timing variables: VISITNUM, VISIT, and VISITDY • What is really the start and end of a visit? • Create Subject Visits dataset from Visit based SDTM datasets

  7. Creating Trial Visits (2) • Planned schedule of Visits • Challenge is in defining start and end of a visit • ARM/ARMCD can be used if schedule varies by Arm

  8. Creating Subject Visits Subject Visits comes from the data, rather than the Visit Start and End Rules.

  9. Screen Run-In Placebo Screen Run-In Drug A Screen Run-In Drug B Trial Arms and Elements Overview Screen Run-in Placebo Trial Elements describes the Elements and the rules for the start and end of each. Drug A Drug B Trial Arms describes the Elements in each Arm, their order and Epoch, and any branching or transition rules. Placebo Drug A Drug B Epochs are described only in Trial Arms, and have no separate table. Screening Run-In Treatment Trial Visits describes the planned Visits for each Arm, and any start and end rules. Visit 1 Visit 2 Visit 3 Visit 4 Visit 5

  10. Screen Run-In Placebo Screen Run-In Drug A Screen Run-In Drug B Creating Trial Arms (1) Placebo Drug A Drug B

  11. Creating Trial Arms (2) • High level treatment plan • Composed of Elements from Trial Elements • Planned ARM values in DM correspond to ARM values in Trial Arms • Names of ARM should reflect the protocol

  12. Creating Trial Elements (1) • Usually the most challenging dataset • Not a duplication of EX (Exposure) • Start rules are the most important • Subject data must exist to support the creation of these • Start of next element defines end of previous

  13. Creating Trial Elements (2) • Simple as possible • Screening Element – use date of informed consent for Start Rule • First Treatment Element - date/time of the first administration of that treatment. • End of the last Element usually coincides with the date/time the subject completes the study • Use pseudocode to facilitate and ensure creation of Subject Elements data

  14. Example pseudocode: EXSTDTC where EXTRT = RUN-IN DRUG Example pseudocode: DSSTDTC where DSDECOD = INFORMED CONSENT Creating Trial Elements (2) Screen Run-in Placebo Trial Elements describes the Elements and the rules for the start and end of each. Drug A Drug B

  15. EXSTDTC where EXTRT = RUN-IN DRUG DSSTDTC where DSDECOD = INFORMED CONSENT Creating Subject Elements (3)

  16. Trial Design Challenges Retrospective creation can be difficult • Finding the information • Protocol • CRFs • Actual Subject Data • Inconsistency in information • Authored by different people • Interpretation

  17. TDM Best Practices • SIMPLE and informative • Create Trial Summary first to become familiar with the Study • Use a Trial Design Template • Use Controlled Terminology

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