Introduction: Challenges in Analyzing and Interpreting Multiple
(Censored) Outcomes in Chronic Disease Trials
Dianne M. Finkelstein, Ph.D., MGH, Harvard University,
Boston, MA, USA
5 August, 2014
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 reintervention, 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
Activity 282 on Aug 5 at 8:30am
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BC Healy Chair
Session 210146