The present article discusses and compares multiple testing procedures (MTP) for controlling Type I error rates defined as tail probabilities for the number (gFWER) and proportion (TPPFP) of false positives among the rejected hypotheses. Specifically, we consider the gFWER- and TPPFP-controlling MTPs proposed recently by Lehmann & Romano (2004) and in a series of four articles by Dudoit et al. (2004), van der Laan et al. (2004b,a), and Pollard & van der Laan (2004). The former Lehmann & Romano (2004) procedures are marginal, in the sense that they are based solely on the marginal distributions of the test statistics, i.e., on cut-off rules for the corresponding unadjusted p-values. In contrast, the procedures discussed in our previous articles take into account the joint distribution of the test statistics and apply to general data generating distributions, i.e., dependence structures among test statistics. The gFWER-controlling common-cut-off and common-quantile procedures of Dudoit et al. (2004) and Pollard & van der Laan (2004) are based on the distributions of maxima of test statistics and minima of unadjusted p-values, respectively. For a suitably chosen initial FWER-controlling procedure, the gFWER- and TPPFP-controlling augmentation multiple testing procedures (AMTP) of van der Laan et al. (2004a) can also take into account the joint distribution of the test statistics. Given a gFWER-controlling procedure, we also propose AMTPs for controlling tail probability error rates, Pr(g(V_n,R_n) > q), for arbitrary functions g(V_n,R_n) of the numbers of false positives V_n and rejected hypotheses R_n. The different gFWER- and TPPFP-controlling procedures are compared in a simulation study, where the tests concern the components of the mean vector of a multivariate Gaussian data generating distribution. Among notable findings are the substantial power gains achieved by joint procedures compared to marginal procedures.


Statistical Methodology | Statistical Theory