It’s about Time! (to Event) An ADTTE Case Study Presented by: Srinivasan Ramasubramanian, Programming Manager, Janssen Kristi Garner, Sr. Statistical Programmer, Theorem Clinical Research

ADTTE CDISC Standard Published in May 2012 Examples provided for: Single event/binary values for censoring variable Single event/multiple values for censoring variable Composite event Designed to support common time-to-event analysis methods

Overview of the Case Study Progression Free Survival (PFS) – “Gold Standard Endpoint” Additional endpoints: OS – TTP – TTT - DoR This presentation walks through how ADTTE for a particular oncology trial was conceptualized and implemented with specific examples.

Highlights of the Case Study Complexities in integrating multiple events of interests into ADTTE without an intermediate dataset. How to derive and map single and composite time to event endpoints. Traceability challenges. A work around to CNSDTDSC (Censor Date Description). Customization to minimize validation challenges. Deciding on Binary or Multiple values for censoring variable (CNSR) Single vs. multiple ADTTE datasets

Trial Specifics Randomized, open label, phase III trial with a control arm and a study drug arm. Primary endpoint is Overall Survival. Secondary endpoints are Progression-free Survival, Objective Response Rate, Time to Progression, Symptom Severity and Safety. Approximate 570 subjects randomized in 2:1 ratio Radiographic assessment of disease obtained every 6 weeks Interim Analysis performed when approx. 188 deaths observed

Time-to-event variables Most variables are common to BDS datasets. AVAL – Analysis Value (Req) STARTDT - Time to Event Origin Date for Subject (Perm) ADT – Analysis Date (Perm) AVISIT – Analysis Visit (Cond) CNSR – Censor (Req for TTE dataset) EVNTDESC - Event or Censoring Description (Perm) CNSDTDSC – Censor Date Description (Perm)

Binary vs. Multiple values for CNSR variable Standard allows for either binary or multiple values for CNSR variable Uniquely identifies various censoring reasons Provides opportunity for further analyses Number of possible values = number of different reasons for censoring Examples of possible CNSR/EVNTDESC values: 0 – Death / 1 – Censored / 3 – Lost to Followup

Binary vs. Multiple values for CNSR variable - 2 Which model fits our trial? Multiple reasons for censoring exist Possible for subject to meet multiple censoring criteria Which analyses will be performed? Statistical analyses require binary data Need to stay ‘one proc away’ Decision made to keep binary model

Single vs. multiple ADTTE datasets New data received/new analyses required Incrementally store data vs. new analysis datasets CDISC guidance allows sponsor discretion ADTTEIDP/ADTTEAN

Challenges in deriving ADTTE without an intermediate dataset.

Multiple sources Each event of interest has a different source. CNSR values were kept the same for “Death” and “Event” (0) for different events of interest. Derivation rules for the same variable were different based on parameters and hence debugging was complicated. (Example: Next slide) Time to Tumor assessment was included in in ADTTE, but, it is not an information that could be classified as neither an event nor censored.

Map single and composite endpoints in one ADTTE dataset

The event date could be one of 'Death Date', 'Date of Disease progression', 'Date of death due to PD' or 'Date of First Anti-cancer therapy'. As we did not have an intermediate dataset to trace the source in selecting the event date, we decided to keep all these four dates in ADTTE. Multiple levels of CNSR was avoided due to increasing complexity.

Handling EVNTDESC(Event or Censoring Description) and CNSDTDSC (Censor Date Description).

Handling EVNTDESC(Event or Censoring Description) and CNSDTDSC (Censor Date Description). Having multiple levels of the censoring and event reason (CNSDTDSC and EVNTDESC) enables reviewers to better understand the data. EVNTDESC was kept at 2 levels as the CNSR variable was used by the TLFs assuming the values would be 0 or 1.

Censoring date description (CNSDTDSC) was not used in the dataset as the event date sources were overlapping. Distinguishing these dates was highly complex due to their derivations and due to no clear differentiation in definition of these dates. Example: Event date for "Overall Survival" could be either DEATHDT and DTHPDDT. Similarly, in the next row, "Progression Free Survival" event date could be either PDDT or ANTICDT.

Customization to minimize validation challenges Complex algorithm for determining censor/event subjects and relevant dates Missed assessment dates Anti-cancer therapy dates Disease Progression Death No assessments before death

Customization to minimize validation challenges - examples Subject Analysis Reference Start Date Parameter Analysis Value Analysis Date Time to Event Origin Date for Subject Censor Date of Death Date of Disease Progression Date of Death due to PD A 15MAY2012 Overall Survival (days) 229 25DEC2012 11MAY2012 Date of the First Anti-Cancer Therapy Missed 2 or More Consecutive Ass. Flag 17JUL2012 Y Subject Analysis Reference Start Date Parameter Analysis Value Analysis Date Time to Event Origin Date for Subject Censor Date of Death Date of Disease Progression Date of Death due to PD A 15MAY2012 Progression-Free Survival (days) 46 25JUN2012 11MAY2012 1 25DEC2012 Date of the First Anti-Cancer Therapy Missed 2 or More Consecutive Ass. Flag 17JUL2012 Y

Summary Biggest challenge Lessons learned