Finkelstein D JSMAug2014 - MGH Biostatistics Center
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Transcript Finkelstein D JSMAug2014 - MGH Biostatistics Center
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
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