5 August, 2014 Introduction: Challenges in Analyzing and Interpreting Multiple (Censored) Outcomes in Chronic Disease Trials Dianne M. Finkelstein, Ph.D.,

5 August, 2014

Introduction: Challenges in Analyzing and Interpreting Multiple (Censored) Outcomes in Chronic Disease Trials

Dianne M. Finkelstein, Ph.D., MGH, Harvard University,

Boston, MA, USA

Multiple Outcomes In Clinical Trials

Trials often have several outcomes that are monitored for treatment benefit

• Survival is primary in a life-threatening disease but may take too long to show benefit

• Other measures of disease progression can be more rapidly sensitive in a trial

How should these be handled in the design, analysis, and report of the trial results?

Examples of Multiple Outcomes

Breast cancer clinical trial

• Survival, recurrence (distant, local), quality of life

• Commonly use progression-free survival as primary

ALS (Lou Gehrig’s Disease)

• ALS functional rating scale (ALSFRS)

• Survival not likely to be affected by Rx

• ALSFRS commonly chosen as primary

Examples of Multiple Outcomes

Heart Failure (CHARM trial)

• Chronic heart failure (death, cardiovascular event), hospitalizations

• Commonly use time to first of cardiac events/death

Scleroderma (interstitial lung disease)

• Pulmonary function (FVC) at 12 months and death

• Commonly combined FVC and death primary

Disability and quality of life secondary

How are multiple endpoints usually handled?

Report univariate analyses with correction for multiplicity (Bonferroni for example)

Select a primary endpoint that guides decision about the treatment

Co-primary outcomes or hierarchy

Alternatively: Test Based on Data From Two or More Endpoints

“Time to first” of two or more events

• Failure-time analysis

• Failure is the first observed

• Example: Progression-free survival in cancer

Global test on first as well as subsequent endpoints

• Can compare patients pair-wise on many events

• Base the test on sum of scores from one group

Advantage of Tests Based on a Combined Outcome

Considering “time to first event” or “global test”

Allows simultaneous evaluation of multiple outcomes without adjustment for multiplicity

Account for effect confounding of outcome measures that are combined

Recovers information from censored or missing data

Can increase power

Co-primary endpoints would require larger N as both must have sufficient power

Problems With “Time to First Outcome” Test

Treats all events equally

Lose information from subsequent or repeated events

• CHF: time to death or first cardiac event loses information on recurrences

• PFS loses information on death for patients with progression observed before death

• Sometimes early events are less important clinically

Could lose power if treatment primarily effects progression, and survival dilutes the test

A Global Test to Combine Mortality and Longitudinal Outcomes in a Clinical Trial

Joint Rank Test of Finkelstein & Schoenfeld 1999 All patients compared pair-wise on time to death

and a longitudinal outcome If cannot compare on death (due to censoring) then

compare on longitudinal outcome at the latest time point that you have data from both subjects

Assigned score of +1 (better) or -1 (worse) or 0 (can’t compare) Test based on sum scores for each patient in

treated group. Wilcoxon test applied

A Global Test to Combine Mortality and Longitudinal Outcomes in a Clinical Trial

(continued)

The F-S Joint Rank Test was first proposed in SIM in 1999 Finkelstein-Schoenfeld

Called Generalized Wilcoxon Test (GGW) in paper or Combined Test in literature

Other papers have re-named the test CAFS (Combined Assessment of Function and

Survival) in Berry et al. (2013) for ALS Win Ratio (Pocock et al for cardiac trails) 2012

Ritesh Ramchandani will discuss a generalization to this later in this session

Other Global Tests:

PC O ’Brien Rank Sum Test (1984) In the pooled sample, patients are ranked on each

outcome Outcome-specific ranks are summed, giving a total rank

for every patient Conduct ANOVA, t-test, or rank-sum test on total ranks Viewed as supplement to univariate results

Estimate Derived from the Global Test Pocock et al. Win Ratio

As for F-S Joint Rank test, count number of pairs where new Rx patient did better (win) or worse (loss)

Win Ratio= number of winners / number of losers Get estimate of proportion who won from WR Allows calculation of confidence bounds and

graphical display of results

Examples of published global endpoint analyses (Barry).1984 Non-parametric and parametric approaches to analyzing multiple endpoints; illustrated using a diabetes trial

1999 Non-parametric evaluation of time to event (e.g. mortality) and longitudinal measure (e.g. change over time in CD4 lymphocyte count in HIV/AIDS)

2000 Analysis of longitudinal measure and event history or survival-schizophrenia trial

2006 Repeated measurements and event time data (shared parameter model)

2007, 2011 Continuous measure over time (e.g. disability index in scleroderma) and time-to-event outcomes (e.g. renal crisis and death in scleroderma)

2007 Longitudinal measurement (e.g. change in percent forced vital capacity) and time to treatment failure or death (in scleroderma-associated lung disease)

2008 Death and another outcome such as stroke, myocardial infarction, time to re-intervention, angina, or hospitalization in cardiovascular clinical trials

2010 Time to loss of virological response; incorporates virological failure assessments, loss to follow-up, new treatment initiation, and death HIV

2013 Amyotrophic Lateral Sclerosis and Frontotemporal Degeneration

Suggestions in Using Global Test

Can use hierarchy to give more importance to some endpoints

Caution about including endpoints that don’t reflect Rx effect as can dilute results

Best to use in the setting that all intermediate endpoints are on the same path (to death)

• Example: tumor progression is predictive of earlier death

• However salvage Rx can diminish protocol Rx impact on death

Suggestions on Global Analyses (continued)

Put global test analysis in the Analysis Plan

Include component-wise analysis plan

Calculate power for the global analysis

Future Research is Needed

Refine the estimate associated with the test

• WIN ratio is affected by the total follow-up time of the study

Graphical display of results

• Depict global estimate over time

• Show what outcomes dominate the score

Testing and estimation of univariate outcomes

• Joint test can be significant but not univariate outcomes

• Hierarchical testing

Conclusions

A global test of multiple outcomes can increase power and assess multi-dimensional treatment outcomes.

Use of joint tests is gaining in popularity in many disease settings (Pharma as well as FDA)

Needs to be considered in more disease settings

New methods and approaches needed to handle the interpretation of global outcome analyses

If you propose a new test, give it a memorable name

Session

1. Andrew Strahs: Illustration of issues that can arise with “time to first” outcome in a clinical trial

2. Ritesh Ramchandani: Generalization of Global test

3. Marc Buyse: Approach to summarizing Global test outcomes in a trial

4. David Schoenfeld: Discussion

SEE OUR WEBSITE FOR THE TALKS AND CONTACTS

Google “MGH Biostatistics” for hedwig.mgh.harvard.edu/

References

1. Finkelstein, DM and Schoenfeld, DA. Combining mortality and longitudinal measures in clinical trials. Statistics in Medicine 1999;18:1341-1354.

2. O’Brien PC, Procedures for comparing samples with multiple endpoints, Biometrics 1984 40(4):1079087.

3. Berry JD, Miller R, Moore DH, Cudkowicz ME, Van Den Berg LH, Kerr DA, Dong Y, Ingersoll EW, Archibald D, “The combined assessment of function and survival (CAFS): A new endpoint for ALS clinical trials, ALS and Frontotemporal Degeneration 2013 14(3): 162-8.

4. Pocock SJ, Cono AA, Collier TJ, Wang, D, “The win ratio: a new approach to the analysis of composite endpoints in clinical trials based on clinical priorities, European Heart Journal 2012 33:176-82.

5. Sun H, Davison BAA, Cotter G, Pencina MJ, Koch GG, “Evaluating treatment efficacy by multiple end points in phase II acute heart failure clinical trials: analyzing data using a global method”, Circulation 2012: 5: 742-9.

Thank you!

Visit our website: hedwig.mgh.harvard.edu/biostatistics/

Examples of Multiple Outcomes in ALS

Phase II Trial of two doses of dexpramipexole for ALS

• Mortality alonse p=.071 ALSFRS slope p=.177

• Joint Rank test p=.046

Simulations

• If one endpoint moderate and one significant, Joint Rank test better

• Moderate on each may not be enough

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