CDISC BrCa TAUG & Oncology Information Session

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2 15 September 2016 CDISC BrCa TAUG & Oncology Information Session Barrie DCDISC 2

3 24 February :00-11:30am CST CDISC Oncology Information Session Rhonda Facile, Ann White, John Owen, Diane Wold, Anthony Chow, Julie Chason, Barrie Nelson, Rene Dahlheimer 3

4 Agenda CDISC Oncology Information Session Membership Oncology Standards Development Plan Status of Oncology Projects Breast, Prostate, Colorectal, Lung Oncology Domains, Terminology & Datasets Q & A 4

5 CDISC Membership Over 85 CDISC Members Specialize in Oncology Colorectal Cancer AbbVie, Accenture, AstraZeneca, FDA, GSK, Johnson & Johnson, inventiv Health, Novartis, Lilly, PRA Health Sciences, Pfizer, Roche, Takeda, The Griesser Group, UCB Prostate Cancer AbbVie, Accenture, Amgen, Astellas, AstraZeneca, FDA, Gilead, GSK, inventiv Health, Jazz Pharmaceuticals, Johnson & Johnson, Lilly, Medivation, Novartis, PAREXEL, PRA Health Sciences, Roche, Sanofi, Takeda, The Griesser Group, Pfizer, UCB Breast Cancer AbbVie, Accenture, Amgen, Astellas, AstraZeneca, FDA, GSK, INC Research, ICON, inventiv Health, Johnson & Johnson, Lilly, Merck, PAREXEL, Pierre Fabre, Pfizer, Novartis, Roche, Sanofi, Theorem Clinical, Takeda, UCB 5

6 Oncology Standards Development Plan 6

7 Oncology Standards Development Plan Focus development of new concepts within an indication Reuse/reference concepts developed in previous User Guides Re-use knowledge/trained resource across ONCO TA projects Liaison with CDISC foundational groups ONCO SDS group ONCO CDASH team ONCO ADaM team 7

8 Oncology Standards Development Plan Breast Cancer Developed a visual document map This will be adapted as content is developed for other indications to become an oncology level map 8

9 Status of Oncology Projects 9

10 CFAST Oncology Program Overview Oct 11

11 Oncology Domains, Terminology & Datasets 12

12 Oncology Domains and Terminology The TAUG-BrCa v1.0 provides advice and examples for Clinical Data Acquisition Standards Harmonization (CDASH), submission data based on the Study Data Tabulation Model (SDTM), and the Analysis Data Model (ADaM), including: Sample case report forms (CRFs) compliant with CDASH, annotated with CDASH and SDTM variables CDASH metadata for the sample CRFs (included in the CDASH Metadata folder of the posting package) Guidance on which domain models and datasets from the SDTM Implementation Guide for Human Clinical Trials (SDTMIG) to use in representing collected data Examples of SDTM datasets, with text describing the situational context and pointing out records of note Variable definition metadata for non-standard (Supplemental Qualifier) variables used in example SDTM datasets and/or CRF mapping annotations Analysis datasets compliant with ADaM, with dataset- and variablelevel metadata Table shells illustrating some kinds of statistical analysis that can be represented in the ADaM datasets 13

13 Oncology Domains and Terminology New terminology has been developed to support Breast Cancer studies and includes new codelists or new values added to existing codelists The SDTM examples spreadsheet has been update to include examples to support Breast Cancer studies The Breast cancer disease recurrence examples can be used as a reference for trials with disease recurrence endpoints in other tumor types. A new proposed ADaM variable is presented in the BrCa TAUG a special purpose parameter qualifying variable. 14

14 Pathology Example: Microscopic Findings This example has been modeled in a fashion consistent with the examples given for the Microscopic Findings (MI) domain in SDTMIG v

15 Prior Treatments Prior chemotherapy or radiotherapy treatments may be of special interest 16

16 Example CRF: Prior Radiotherapy 17

17 Example Data: Prior Radiotherapy 18

18 Example Data: Prior Medications 19

19 Example: Treatments 20

20 Example CRF: Target Lesion 21

21 Example CRF: Non-Target Lesion 22

22 Example CRF: New Lesion 23

23 CRF Example: Disease Response 24

24 Example Data: Tumor Identification 25

25 Example Data: Disease Response relrec.xpt pr.xpt Row STUDYID RDOMAIN USUBJI IDVARV RELTYP IDVAR D AL E RELID 5 VWX7777 TU TUREFID MANY A 6 VWX7777 PR PRREFID ONE A 7 VWX7777 PRLNKG PR RP MANY D 8 VWX7777 RSLNKG RS RP ONE D Row STUDYID DOMAIN USUBJID PRSEQ PRREFID PRLNKGRP PRTRT PRPRESP PROCCUR PRLOC EPOCH 1 VWX7777 PR 2 VWX7777 PR 3 VWX7777 PR 4 VWX7777 PR 5 VWX7777 PR 6 VWX7777 PR VWX VWX VWX VWX VWX VWX IMG A1 CT SCAN Y Y MULTIPLE SCREEN 2 IMG A1 SCINTIGRAPHY Y Y BONE SCREEN 3 IMG A2 CT SCAN Y Y MULTIPLE TREATMENT 4 IMG A2 SCINTIGRAPHY Y N BONE TREATMENT 5 IMG A3 CT SCAN Y Y MULTIPLE TREATMENT 6 IMG A3 SCINTIGRAPHY Y Y BONE TREATMENT

26 Data Example: Disease Recurrence 27

27 Data Example: Disease Recurrence 28

28 Example Data: ADSL One record per subject Should include expected variables, population flags, covariates, key information pertaining to the cancer and key prior treatment dates 29

29 Why ADEVENT? Various event dates can be used to determine endpoint Event dates can come from a variety of data sources How do you convince others you selected the correct date? Helps with traceability 30

30 Example Data: ADEVENT 31

31 Time to Event Key component Time to event start date Date of event or censoring (date of last adequate assessment) Events to consider Actual event Other events 32

32 Example Data: ADTTE and ADRESP 33

33 Thank you! Questions? 34