Peer-review started: October 27, 2014
First decision: November 27, 2014
Revised: December 19, 2014
Accepted: December 29, 2014
Article in press: January 4, 2015
Published online: January 26, 2015
Processing time: 96 Days and 4.3 Hours
In clinical trials, the primary efficacy endpoint often corresponds to a so-called “composite endpoint”. Composite endpoints combine several events of interest within a single outcome variable. Thereby it is intended to enlarge the expected effect size and thereby increase the power of the study. However, composite endpoints also come along with serious challenges and problems. On the one hand, composite endpoints may lead to difficulties during the planning phase of a trial with respect to the sample size calculation, as the expected clinical effect of an intervention on the composite endpoint depends on the effects on its single components and their correlations. This may lead to wrong assumptions on the sample size needed. Too optimistic assumptions on the expected effect may lead to an underpowered of the trial, whereas a too conservatively estimated effect results in an unnecessarily high sample size. On the other hand, the interpretation of composite endpoints may be difficult, as the observed effect of the composite does not necessarily reflect the effects of the single components. Therefore the demonstration of the clinical efficacy of a new intervention by exclusively evaluating the composite endpoint may be misleading. The present paper summarizes results and recommendations of the latest research addressing the above mentioned problems in the planning, analysis and interpretation of clinical trials with composite endpoints, thereby providing a practical guidance for users.
Core tip: When planning a clinical trial with a composite primary endpoint: (1) Be aware of planning uncertainties when calculating the sample size and incorporate them in an adequate way; (2) Include a multiple testing strategy for an improved interpretation of the study results; (3) Take into account competing risks when analyzing the individual components of a composite endpoint; and (4) Analyze subsequent events in an adequate multi-stage model.