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Generalized pairwise comparison methods to analyze (non)prioritized composite endpoints.

J Verbeeck1, E Spitzer2,3, T de Vries2

  • 1I-BioStat, Universiteit Hasselt, Hasselt, Belgium.

Statistics in Medicine
|October 30, 2019
PubMed
Summary
This summary is machine-generated.

Generalized pairwise comparison methods offer a comprehensive analysis of composite endpoints in clinical trials, accounting for event severity and multiplicity. Prioritized methods are sensitive to component ranking, while non-prioritized tests provide a more robust evaluation.

Keywords:
composite endpointgeneralized pairwise comparisonlogranknet benefitwin ratio

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Area of Science:

  • Clinical Trials Methodology
  • Biostatistics
  • Longitudinal Data Analysis

Background:

  • Traditional time-to-first-event analyses (logrank, Cox) inadequately address the complexity of composite endpoints.
  • Composite endpoints often involve multiple events with varying importance and severity, which standard methods overlook.
  • There is a need for analytical methods that can incorporate the full spectrum of outcomes within a composite endpoint.

Purpose of the Study:

  • To summarize and compare generalized pairwise comparison methods for composite endpoint analysis.
  • To evaluate the impact of component correlation on the power of these methods.
  • To assess the performance of prioritized versus non-prioritized generalized pairwise comparison tests.

Main Methods:

  • Summarized four generalized pairwise comparison methods: Finkelstein-Schoenfeld, Buyse, unmatched Pocock, and adapted O'Brien tests.
  • Conducted simulation studies to compare these methods against each other and the logrank test.
  • Specifically investigated the effect of correlation between composite endpoint components on statistical power.

Main Results:

  • Prioritized generalized pairwise comparison methods demonstrated similar performance but were sensitive to the ranking of endpoint components.
  • These prioritized methods did not fully capture treatment effects beyond the highest-ranked component.
  • The non-prioritized pairwise comparison test showed robustness, with correlation primarily affecting its variance, not its fundamental ability to detect effects.

Conclusions:

  • Generalized pairwise comparison methods provide a more nuanced analysis of composite endpoints than traditional methods.
  • Prioritized methods require careful consideration of component ordering, while non-prioritized methods offer broader applicability.
  • These advanced methods allow for the inclusion of diverse outcome types, enabling a more complete evaluation of treatment effects.