1. ADaM Standards Wouter van Wyk

2. Why ADaM –SDTM purpose is to provide collected data Not designed for ease of analysis –ADaM purpose is to provide data that is ready for analysis Is designed for ease of analysis “1 proc away” –ADaM helps the reviewer trace: What you said you would do What you did –TRACEABILITY is of utmost importance

3. What is Traceability? –Traceability permits the understanding of the relationship between the analysis results, the analysis datasets and the SDTM datasets. –Traceability is built by clearly establishing the path between an element and its predecessor(s) –Traceability establishes across-dataset relationships, as well as within- dataset relationships. –Traceability = Transparency

4. What is Traceability? Analysis ResultAnnotated shellsADaM Dataset ADaM Specs (Define.xml) SDTM Dataset Traceability variables (in dataset) It should be easy to trace where a value came from

5. 2 Kinds of Traceability –Metadata traceability How was a variable or observation derived? ADaM Specifications, Define.xml –Data point traceability Which observations were used to derive a value of observation? Traceability variables in the ADaM dataset E.g. SRCVAR, SRCDOM, SRCSEQ

6. ADaM Rules for Analysis Datasets –At least ADSL should be created –Not all data from SDTM needs to go into ADaM –There is not a 1:1 relationship between ADaM and SDTM 1 SDTM dataset could map to multiple ADaM Datasets Many SDTM datasets could map to the same ADaM Dataset Some of the SDTM data will be present in more than 1 ADaM Dataset –Create the optimum number of ADaM datasets as required for analysis E.g. If Medical History is not summarized, you don’t need to create an ADaM dataset for it.

7. ADaM Rules for Analysis Datasets –If the variable is straight from SDTM it must have the same attributes must have the same contents may not be modified in any way –See the ADaM Implementation Guide on naming conventions and controlled terminology.

8. Classes of ADaM structures –Subject Level Structure (ADSL) Reserved dataset name ‘ADSL’ One record per subject, regardless of study design –Basic Data Structure (BDS) Designed with the majority of analysis files in mind One or more records per subject per analysis parameter, per analysis time point (if applicable) Most if not all the by Visit domains (Findings) from SDTM would fit in this structure Adverse Events does not fit the BDS Structure –You can create your own class if these 2 are not appropriate

9. SDTM and ADaM “Core” Definitions SDTM “Core” DefinitionsADaM “Core” Definitions Required Must be present and must be populated Required Must be present Expected Any variable necessary to make a record meaningful in the context of a specific domain Must be present (but may be blank) Conditionally Required if applicable Must be present if applicable (e.g. “Actual Treatment” would be required if patients did not receive the “Planned Treatment”) Permissible Any variable that is appropriate when collected or derived (remove if blank) Permissible May be present

10. Analysis Data Subject Level - ADSL –Contains important information about a subject –Structured as one record per subject Regardless of clinical trial design Allows for easy merging with any other ADaM or SDTM dataset –Used as a source for Variables required in other analysis datasets Denominator values for populations of interest –Contains some Required variables

11. Analysis Data Subject Level - ADSL –Primary source of subject-level variables included in other analysis datasets –Variables describe attributes of a subject as well as the subject’s experience in trial (e.g. date of death) –Not intended to be the only file that supports subject level data E.g. if efficacy analysis is time to event, create ADTTE file instead of putting everything in ADSL –Standard Disposition and Demographics tables should be able to be created from ADSL

12. What goes into ADSL? –Study and subject identifiers –Subject demographics –Population indicators –Treatment variables –Trial dates –Stratification variables –Treatment duration and possibly compliance variables –See the ADaM IG for possible ADSL variables

13. Basic Data Structure - BDS –Flexible enough for the majority of analyses –Vertical structure: can be thought of as one or more records per subject (USUBJID) per analysis parameter (PARAM, PARAMCD) per analysis time point (AVISIT, ADY) –Even though similar in structure to the SDTM Findings domains (think of VS, LB and EG) if is not a replica and could contain any type of data An Event A Finding An Intervention Completely derived observation that is created from multiple types of SDTM data

14. Basic Data Structure - BDS –The BDS domains have an “Analysis-focused” design One proc away –Supportive columns may be added as appropriate –Named ADxxxxxx, where then ‘x’ denotes a name of your choosing ADVS ADLB ADTTE ADEFF

15. What goes into BDS? –ADSL Variables Keep only what is needed STUDYID, USUBJID at minimum –BDS Analysis Parameter Variables –BDS Analysis Value Variables –BDS Analysis Timing Variables –BDS Analysis Descriptor Variables –BDS Indicator Variables –BDS Analysis Population Indicators –BDS Analysis Enabling Variables –BDS Data Point Traceability Variables

16. BDS Analysis Parameter Variables –Variables to describe what is being analyzed –Required PARAM - Parameter Description –Value of PARAM should be what appears on statistical tables –Uniquely describes what is contained in the Analysis variable –Includes: test name, unit, position, location etc PARAMCD – 8 character version of PARAM –Other PARAMN

17. BDS Analysis Value Variables –Variables that contain numeric of character values that are analyzed –At least one of the following is required AVAL: numeric result as described by PARAM AVALC: character result as described by PARAM –Other BASE: numeric baseline result BASEC: character baseline result CHG: change from baseline PCHG: Percent change from baseline SHIFTy: function of defined pairs, –Such as shift from baseline - ‘Normal to Abnormal’

18. Some BDS Analysis Descriptor Variables –Use when there are derived observations within a PARAM DTYPE: e.g. LOCF, WOCF, AVERAGE –Use where there is windowing AWRANGE, AWLO, AWHI: Analysis window valid range AWTARGET: Analysis window target day AWDIFF: Difference from target day AWU: Unit of AWLO, AWHI –Use in time-to event analysis STARTDT: Original date of risk for the TTE analysis CNSR: Defines whether the event is censored EVENTDESC: Description of the event of interest or censoring reason The actual time to event result will be in AVAL

19. BDS Indicator Variables –Conventions: Character variable will end in *FL –Y/N/Null Numeric variable will end in *FN –1/0/Null –Examples: ABLFL: Baseline flag ANLzzFL: Analyzable flag ONTRTFL: On-treatment flag

20. BDS Data Point Traceability Variables –A variable is supportive if it is not required in order to perform an analysis but is included in order to facilitate traceability –Retaining certain SDTM variables --SEQ, VISIT, VISITNUM, --DY, ARM, ARMCD\ –Additional variables SRCDOM: Domain of origin for value contained in AVAL (e.g. ”LB”) SRCVAR: Variable name used to create AVAL (e.g. “LBSTRESN”) SRCSEQ: Sequence number to identify record used (e.g. = LBSEQ)

21. What to do with Adverse Events? –Some kinds of data do not fit in the BDS Structure E.g. Adverse Events, Concomitant Medications –Create your own structure based on ADaM principles Dataset name should start with “AD” SDTM variables should not be modified, rather create new variable and keep the SDTM variable for traceability Dataset should be analysis ready, one proc away Follow naming conventions Ensure traceability back to the source data –ADAE could mean you only need to merge ADSL with SDTM.AE and create a few numeric dates as needed, but will be driven by what analyses need to be done.

22. Some useful websites –www.cdisc.org/adamwww.cdisc.org/adam –www.lexjansen.comwww.lexjansen.com –www.phuse.euwww.phuse.eu

23. Any Questions?