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

0 25DEC2012

25DEC2012

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

25DEC2012

Date of the First Anti-Cancer Therapy

Missed 2 or More Consecutive Ass. Flag

17JUL2012 Y

Summary

Biggest challenge Lessons learned