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In the analysis of composite endpoints in a clinical trial, time to first event analysis techniques such as the logrank test and Cox proportional hazard test do not take into account the multiplicity, importance, and the severity of events in the composite endpoint. Several generalized pairwise comparison analysis methods have been described recently that do allow to take these aspects into account. These methods have the additional benefit that all types of outcomes can be included, such as longitudinal quantitative outcomes, to evaluate the full treatment effect. Four of the generalized pairwise comparison methods, ie, the Finkelstein-Schoenfeld, the Buyse, unmatched Pocock, and adapted O'Brien test, are summarized. They are compared to each other and to the logrank test by means of simulations while specifically evaluating the effect of correlation between components of the composite endpoint on the power to detect a treatment difference. These simulations show that prioritized generalized pairwise comparison methods perform very similarly, are sensitive to the priority rank of the components in the composite endpoint, and do not measure the true treatment effect from the second priority-ranked component onward. The nonprioritized pairwise comparison test does not suffer from these limitations and correlation affects only its variance.
Verbeeck et al. (Mon,) studied this question.