One-sided multiple endpoint testing in two-sample comparisons

Abstract

This article reviews several methods for comparing two treatments with multiple endpoints that take into account the dependence structure among the endpoints and that are sensitive to the multivariate one-sided alternative. The methods are classified into procedures that adjust sin-gle endpoint P-values separately, into bootstrap procedures that adjust single endpoint P-values through resampling from the whole data, and into procedures that summarize the data into a global test statistic. In this context, we describe step-down procedures leading to conclusions about the single endpoints. Applying the closed test principle, James (1991, Statistics in Medicine 10, 1123-1135)-based, O'Brien (1984, Biometries 40, 1079-1087)-based, Tang, Gnecco, and Geller (1989, Biometrika 76, 577-583)-based, and Westfall and Young 1989, Jour-nal of the American Statistical Association 84, 780-786)-based proce-dures are investigated and compared to standard techniques. Monte Carlo simulations for small to moderate sample size...