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Use of composite endpoints in clinical trials
Author(s) -
Sankoh Abdul J.,
Li Haihong,
D'Agostino Ralph B.
Publication year - 2014
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.6205
Subject(s) - clinical endpoint , clinical trial , endpoint determination , sample size determination , surrogate endpoint , selection (genetic algorithm) , medicine , computer science , statistics , medical physics , mathematics , machine learning
The success of a confirmatory clinical trial designed to demonstrate the efficacy of a new treatment is highly dependent on the choice of valid primary efficacy endpoint(s). The optimal clinical and statistical situation for the design of such a trial is one that starts with the selection of a single primary efficacy endpoint that completely characterizes the disease under study, admits the most efficient clinical and statistical evaluation of treatment effect, and provides clear and broad interpretation of drug effect. For diseases with multidimensional presentations, however, the selection of such an endpoint may not be possible, and so drug effectiveness is often characterized by the use of composite or multiple efficacy endpoint(s). The use of a composite endpoint with components that are only slightly correlated but not quite dissimilar in their recognized clinical relevance could lead to a more sensitive statistical test and thus, adequately powered trials with smaller sample size. This note discusses the utility of composite endpoints in clinical trials and some of the common approaches for dealing with multiplicity arising from their use. Copyright © 2014 John Wiley & Sons, Ltd.