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SDTM Trial Design Model: Importance, Structure, and Implementation

Learn about the importance of the SDTM Trial Design Model (TDM) in CDISC electronic data submissions to the FDA and its role in deriving other SDTM and ADaM data sets. Understand the structure and key elements of the TDM and the challenges in implementing it. Discover how the TDM can be used to determine study similarity for data pooling and ensure consistency across clinical trials.

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SDTM Trial Design Model: Importance, Structure, and Implementation

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  1. An Approach-Creating Trial Design Models Jan 2015 Peter Mesenbrink Sangeeta Bhattacharya

  2. Why is the Trial Design Model Important? • SDTM Trial Design Model is a required part of all CDISC SDTM electronic data submissions to the FDA • The metadata defined for Trial Elements (TE), Trial Arms (TA), Trial Visits (TV), Trial Summary (TS) and Trial Inclusion/Exclusion (TI) is converted into SAS data sets and included as part of the Case Report Tabulations that are part of the eCTD submitted to the FDA • It is used the derivation of other SDTM and ADaM data sets • Subject Elements (SE) – Planned and unplanned subject-level trial elements • Subject Visits (SV) – Planned and unplanned subject-level trial visits • Planned and actual treatment groups in ADSL | Overview of Trial Design Model | Business Use Only

  3. Trial Design Model - Purpose Define the study-level visit structure Define a subject’s planned path through the study so that study treatment can be packaged and the groups of subjects to be compared can be defined Provide summary information that is useful in understanding key features of the trial design. This is important in setting up the statistical analysis plan and comparing studies with similar endpoints within a specific disease area. Overview of TDM | Overview of Trial Design Model | Business Use Only

  4. Trial Design Domain Overview • What “is planned” includes Trial Design domains: • 5 CDISC domains • What “actually happened” includes 2 Special Purpose domains: • 2 CDISC subject-level data domains Subject Elements Subject Visits Special Purpose | Overview of Trial Design Model | Business Use Only

  5. Trial Design Domains – What Is Planned | Overview of Trial Design Model | Business Use Only

  6. Special Purpose Domains – What Actually Happened | Overview of Trial Design Model | Business Use Only

  7. Data Flow in Novartis Study Area = SDTM+ Much is changing because of CDISC and Novartis Clinical Data Standards (NCDS) – new systems and applications, new / revised processes and roles, etc. | Overview of Trial Design Model | Business Use Only

  8. Challenges with Implementing Trial Design Every person could have a different interpretation of how to define trial design metadata. Thus as part of project-level planning, teams need to define conventions to be applied across all studies. Until a fully automated tool is developed there will be some work involved particularly for information that require extraction of text from the protocol and/or is not managed by controlled terminology. Model will continue to evolve over time as other knowledge is built-up on the availability of searchable metadata within and across clinical trials. | Overview of Trial Design Model | Business Use Only

  9. Trial design model metadata should be a key source in determining the similarity of clinical studies for data pooling Trial Elements (TE) ELEMENT and ETCD used in definition of treatment group descriptions in drug packaging and analysis, inconsistency will necessitate mapping for pooled analyses Start and End rules (TESTRL, TEENRL) – Should be written for easy translation into programmable rules at the subject level in SE. When subjects receive more than one treatment, consistent definitions will simplify how AEs are counted across treatment groups. Also need to ensure that there is no gaps in time when one element ends and the next element starts Trial Arms (TA) EPOCH appears in every SDTM data set There are certain epoch names that are used in Oracle Clinical (OC) that should never appear in the TDM (UNPLANNED and SUMMARY) ARMCD can be simplified if is difficult to include all treatment elements in 20 characters or less Areas of focus for TDM consistency across studies (1/2)

  10. Trial Visits (TV) Planned visit names (VISIT) should be identical between protocol, TDM, and clinical trial database to ensure that all planned and unplanned visits can be merged at the subject level and for easy translation into analysis visits in ADaM Start rules (TVSTRL, TVENRL) – Need to make clear when assessments taken count towards the planned visit so that unplanned visits can be kept to a minimum Trial Inclusion/Exclusion (TI) Does not need to match identically with what is in the protocol (e.g. can remove parenthetical text as needed to get criterion down to 200 characters) The number of the criterion in the protocol should match the number of the criterion in the TDM (e.g. Inclusion criterion #5 in the protocol, should have IETESTCD = INCL05 in the TDM) Trial Summary (TS) Will be updated several times during the course of the study If TSVAL is not known for a particular TSPARM leave blank and populate TSVALNF accordingly Areas of focus for TDM consistency across studies (2/2)

  11. Timing of Creation | Overview of Trial Design Model | Business Use Only

  12. What have we taken into Account? • Learnings from recent BLAs/NDAs on the content of the SDTM trial design model data sets • Supplemental qualifiers not allowed for SDTM trial design model data sets (i.e. the data elements are fixed and cannot be added to) • Changes and improved understanding of the end-to-end data flow and how the metadata supports it. • To maximize the amount of information in the TDM that can be populated through standard macros and drop down codelists and minimize the amount of manual entry and subsequent re-work • Separate TDM requirements from PK merge requirements 12

  13. How Have we defined a Process • Defined a template (excel spreadsheet with visual basic macros) • To be Completed by Statisticians and Programmers • Easy export of different domains to create the necessary SAS data sets • Template Stored in GPS(Unix-Our Statistical Programming Environment) • Simplified versioning and approval process • PDF rendition signed by lead statistician and lead statistical programmer • Trial visits to be brought back into Oracle Life Science Hub (LSH) for visit numbering/re-numbering as .csv file with special delimiters 13

  14. What have we defined in terms of Roles & Responsibilities • Shared responsibility of TDM development between trial statistician and trial programmer • Primary accountability is as follows: • Trial Visits – Trial programmer in collaboration with Lead Data Manager (LDM) and Trial Statistician • Trial Elements, Trial Arms, Trial Inclusion/Exclusion, Trial Summary – Trial Statistician (Collaboration with LDM on Trial Inclusion/Exclusion) • Easier export of different domains to create the necessary SAS data sets by Study Programmer • Simplified versioning and approval process • Working copy versioned in GPS II /util directory for the clinical study by Study Statistician • PDF rendition signed by lead statistician and lead statistical programmer • Trial visits to be brought back into LSH for visit numbering/re-numbering as .csv file by LeadData Manager 14

  15. Other ways that TDM process will hopefully be improved in the near future • Information to be added in disease level/study level analysis plans to define naming conventions for text that is not automated or managed by codelists/controlled terminology • Standardization of start and end rules for trial elements and trial visits (e.g. will the randomization visit be called “RANDOMIZATION” or “BASELINE”, will treatment trial elements always start with the first dose of study treatment?) • Naming conventions for visit names and treatment elements 15

  16. Explanation of the Process 16

  17. Instructions for completing the template (1/2) 17

  18. Instructions for completing the template (2/2) 18

  19. Macros tab drives generation of TA and TV (1/2) 19

  20. Macros tab drives generation of TA and TV (2/2) 20

  21. Defining Trial Elements first in TE 21

  22. Instructions for completing the template (1/2) 22

  23. Instructions for completing the template (2/2) 23

  24. Macros tab drives generation of TA and TV (1/2) 24

  25. Macros tab drives generation of TA and TV (2/2) 25

  26. Trial Elements 26

  27. Defining Trial Arms and Epochs 27

  28. TA after providing macro information 28

  29. Defining Trial Visits 29

  30. TV after providing macro information 30

  31. TS and using the controlled terminology 31

  32. The SAS format row tells you the format and length of the value 32

  33. Trial Inclusion/Exclusion simplified 33

  34. TI after extracting information from the protocol 34

  35. Export TDM | Presentation Title | Presenter Name | Date | Subject | Business Use Only

  36. What does the future hold? | Presentation Title | Presenter Name | Date | Subject | Business Use Only • Further automated solutions continue to be developed either stand alone or integrated within eProtocol solutions • Challenges remain in having a solution that works in all situations particularly in event-driven and adaptive trial designs but will improve in the future with: • Disease-level structured protocols • Therapeutic area standards which will increase the consistency and allow for the development of disease-level trial design model shells

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