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Biometrical J 2002 Jul;44(5):541-57

Testing for Treatment Effects on Subsets of Endpoints

Chen JJ, Wang SJ


Multiple endpoints are tested to assess an overall treatment effect and also to identify which endpoints or subsets of endpoints contributed to treatment differences. The conventional p-value adjustment methods, such as single-step, step-up, or step-down procedures, sequentially identify each significant individual endpoint. Closed test procedures can also detect individual endpoints that have effects via a step-by-step closed strategy. This paper proposes a global-based statistic for testing an a priori number, say, r of the k endpoints, as opposed to the conventional approach of testing one (r = 1) endpoint. The proposed test statistic is an extension of the single-step p-value-based statistic based on the distribution of the smallest p-value. The test maintains strong control of the FamilyWise Error (FWE) rate under the null hypothesis of no difference in any (sub)set of r endpoints among all possible combinations of the k endpoints. After rejecting the null hypothesis, the individual endpoints in the sets that are rejected can be tested further, using a univariate test statistic in a second step, if desired. However, the second step test only weakly controls the FWE. The proposed method is illustrated by application to a psychosis data set.

Category: Journal Article
DOI: 10.1002/1521-4036(200207)44:5<541::AID-BIMJ541>3.0.CO;2-0
Includes FDA Authors from Scientific Area(s): Toxicological Research Drugs
Entry Created: 2013-01-03