1. Traceability Look for the source of your analysis results
Herman Ament, Cromsource CDISC UG Milan 21 October 2016

2. Contents Introduction, history and CDISC Traceability Examples
Conclusion

3. Introduction, History
In the past submission approvals consisted of a truck full of paper to demonstrate the efficacy and safety of a (new) drug.
Around 2000, instead of submissions on paper, submission via digital data carriers became more and more the standard.
Problem: each company had their own standard. FDA spent 85% of their time to understand the data. Only 15% time left for doing the analysis.
Feb 2000 – Clinical Data Interchange Standards Consortium (CDISC) was formed as an Independent, non-profit organization.
February 2005, release version 1.0 of Define.xml.

4. Introduction, History cont’d
Around 2000 stagnation of New Drug Applications (less Block Busters).
2004, FDA Critical Path Initiative (CPI). To drive innovation in the scientific processes within the Pharma Industry.
FDA: Allegations to overlook safety concern and to give approval to unsafe drugs (example: Vioxx 2005).
The last 10 years, more and more information has to be provided to the authorities, in order to demonstrate the efficacy and safety and to show transparency of the data submitted.
Vioxx = COX-2 inhibitoriox

5. CDISC
CDISC is the source for the standard for clinical data within the Pharmaceutical Industry.
Standardisation of machine readable data SEND, CDASH, SDTM, ADaM …
Standardisation of human readable data (define.xml).
Standardisation of Controlled Terminology.
Many Implementation Guides (IG), including Analysis Data Reviewers Guide (ADRG)
Among this all, the principles of GCP: Traceability and Transparency.
ICH: strongly involved
SEND=Standard for Exchange of Nonclinical Data, CDASH=Clinical Data Acquisition Standards Harmonization, SDTM=Study Data Tabulation Model, ADaM=Analysis Data Model
International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH)

7. Traceability, ADaM IG 1.1, Definition IG=Implementation Guide
Traceability – The property that enables the understanding of the data’s lineage and/or the relationship between an (data) element and its predecessor(s).
Traceability facilitates transparency, which is an essential component in building confidence in a result or conclusion.
Ultimately, traceability in ADaM permits the understanding of the relationship between the analysis results, the ADaM datasets, the SDTM datasets, and the data collection instrument.
Traceability is built by clearly establishing the path between an (data) element and its immediate predecessor.

8. Traceability, ADAM IG cont’d
The full path is traced by going from one element to its predecessors, then on to their predecessors, and so on, back to the SDTM datasets, and ultimately to the data collection instrument.
IG: Section 2.2:
To assist review, ADaM datasets and metadata must clearly communicate how the ADaM datasets were created. The verification of derivations in an ADaM dataset requires having at hand the input data used to create the ADaM dataset. A CDISC-conformant submission includes both SDTM and ADaM datasets; therefore, it follows that the relationship between SDTM and ADaM must be clear. This requirement highlights the importance of traceability between the analyzed data (ADaM) and its input data (SDTM).

9. Traceability, 2 levels, Metadata level
Metadata traceability facilitates the understanding of the relationship of the analysis variable to its source dataset(s) and variable(s) and is required for ADaM compliance. This traceability is established by describing (via metadata) the algorithm used or steps taken to derive or populate an analysis variable from its immediate predecessor. Metadata traceability is also used to establish the relationship between an analysis result and ADaM dataset(s).

10. Traceability, BMI Metadata level example
Collected on eCRF: Height and Weight
Specified in SAP: Demographic and Other Baseline Characteristics
In ADaM Define.xml, relation to SDTM and algorithm

11. Traceability, 2 levels, Datapoint level
Datapoint traceability points directly to the specific predecessor record(s) and should be implemented if practical and feasible. This level of traceability can be very helpful when trying to trace a complex data manipulation path. This traceability is established by providing clear links in the data (e.g., use of SEQ variable) to the specific data values used as input for an analysis value. The BDS and OCCDS structures were designed to enable datapoint traceability back to predecessor data.
BDS=Basic Data Structure OCCDS=ADaM Occurrence Data Structure

12. Traceability, 2 levels, Datapoint level
Typical examples of variables from SDTM domains that help in establishing traceability in ADaM are
Sequence Variables (--SEQ),
Sponsor Defined Identifiers (--SPID),
Group Identifiers (--GRPID),
Timing Variables (VISIT, VISITNUM, EPOCH, --DTC, DY) etc.
Examples of additional variables that can be added in ADaM to achieve some level of traceability are
Analysis Flag variables (ANLzzFL) - to indicate the records that were chosen for analysis among the multiple visits that fall within the same analysis time point windows,
Criterion variables CRITy - text description defining the conditions necessary to satisfy the presence of the criterion and CRITyFL - character indicator of whether the criterion described in CRITy was met.
If additional records were added to analysis datasets for analyses purposes, to establish traceability, BDS allows the usage of variable DTYPE
DTYPE (Derivation Type) which precisely populates the derivation algorithm used to derive an analysis value.
source paper SAS Global Forum 2012

13. ADRG, Analysis Data Reviewers Guide
Purpose The Analysis Data Reviewer’s Guide (ADRG) provides FDA Reviewers with additional context for analysis datasets (AD) received as part of a regulatory submission. The ADRG is intended to describe analysis data submitted for an individual study in the Module 5 clinical section of the eCTD.
The ADRG purposefully duplicates limited information found in other submission documentation (e.g., the protocol, statistical analysis plan, clinical study report, define.xml) in order to provide FDA Reviewers with a single point of orientation to the analysis datasets.
The submission of a reviewer guide does not obviate the requirement to submit a complete and informative define.xml document to accompany the analysis datasets.

14. ADRG, Analysis Data Reviewers Guide
ADRG, submission of the results
Program statements
Example from ADRG

15. Traceability flowchart
Data Management
SDTM
ADAM

16. Traceability, Results Example
Primary objective in the PROTOCOL. To evaluate the superiority of XXX versus YYY in terms of FEV1 AUC0-12h normalised by time on Day 42
Primary efficacy variable in SAP FEV1 (L) (both in terms of absolute values and change from baseline) will be summarised by summary statistics (including the 95% CI of the mean) at Day 1 and 42 and all scheduled time points by treatment for the ITT and PP population. The primary efficacy endpoint (FEV1 AUC0-12h normalised by time on Day 42) will be summarised using summary statistics (including the 95% CI of the mean) and will be displayed by treatment for the ITT and PP population.
Parameter, FEV1 AUC0-12h normalised by time
Time point: Day 42

17. Traceability, Results Example
Parameter, FEV1 AUC0-12h normalised by time Timepoint: Day 42.
Source reference to appropriate listing.
Additional traceability in the footnotes

18. Traceability, Results Example

19. Traceability, Data Example
SAP Definition of FEV1 AUC0-12h normalised by time

20. Traceability, SDTM

21. Traceability, ADaM AVAL/AVALC, source result where XLTESTC=“FEV1”
Criterion flag is used to specify whether the observation is used in the analyses

22. Traceability, SDTM + ADaM
SDTM data, source
ADAM data, PARAMTYP and CRIT1FL

23. Conclusion Building in traceability is challenging job.
Starts at Protocol and ends at submission.
Traceability should be in the documents and in the data.
CDISC enables to build in traceability with variables like PARAMTYPE, DTYPE, --SEQ and many other.
Traceability is a joined effort across departments
project management, data management, biostatistics, medical writing
Resources.
CDISC organisation. Become a member!
Phusewiki
Proceedings SAS Global Forum, SUGI, PharmaSUG, NESUG, SESUG, PhUSE, WUSS, MWSUG, PNWSUG and SCSUG.

24. Herman Ament | Senior SAS Programmer
THANK YOU!
Herman Ament | Senior SAS Programmer
Ph +31 (0) ext. 2812