The Fallback Procedure for Evaluating a Single Family of Hypotheses

ABSTRACT In testing multiple hypotheses, control of the familywise error rate is often considered. We develop a procedure called the “fallback procedure” to control the familywise error rate when multiple primary hypotheses are tested. With the fallback procedure, the Type I error rate (α) is partitioned among the various hypotheses of interest. Unlike the standard Bonferroni adjustment, however, testing hypotheses proceeds in an order determined a priori. As long as hypotheses are rejected, the Type I error rate can be accumulated, making tests of later hypotheses more powerful than under the Bonferroni procedure. Unlike the fixed sequence test, the fallback test allows consideration of all hypotheses even if one or more hypotheses are not rejected early in the process, thereby avoiding a common concern about the fixed sequence procedure. We develop properties of the fallback procedure, including control of the familywise error rate for an arbitrary number of hypotheses via illustrating the procedure as a closed testing procedure, as well as making the test more powerful via alpha exhaustion. We compare it to other procedures for controlling familywise error rates, finding that the fallback procedure is a viable alternative to the fixed sequence procedure when there is some doubt about the power for the first hypothesis. These results expand on the previously developed properties of the fallback procedure (Wiens, 2003). Several examples are discussed to illustrate the relative advantages of the fallback procedure.

[1]  O. Kempthorne The Design and Analysis of Experiments , 1952 .

[2]  Margaret J. Robertson,et al.  Design and Analysis of Experiments , 2006, Handbook of statistics.

[3]  K. Gabriel,et al.  On closed testing procedures with special reference to ordered analysis of variance , 1976 .

[4]  S. Holm A Simple Sequentially Rejective Multiple Test Procedure , 1979 .

[5]  Donald B. Rubin,et al.  Ensemble-Adjusted p Values , 1983 .

[6]  R. Simes,et al.  An improved Bonferroni procedure for multiple tests of significance , 1986 .

[7]  Y. Hochberg A sharper Bonferroni procedure for multiple tests of significance , 1988 .

[8]  G. Hommel A stagewise rejective multiple test procedure based on a modified Bonferroni test , 1988 .

[9]  G. Hommel A comparison of two modified Bonferroni procedures , 1989 .

[10]  P Bauer,et al.  Multiple testing in clinical trials. , 1991, Statistics in medicine.

[11]  G Wassmer,et al.  Procedures for two-sample comparisons with multiple endpoints controlling the experimentwise error rate. , 1991, Biometrics.

[12]  An extended Simes' test , 1994 .

[13]  Bertram Pitt,et al.  Randomised trial of losartan versus captopril in patients over 65 with heart failure (Evaluation of Losartan in the Elderly Study, ELITE) , 1997, The Lancet.

[14]  Y. Benjamini,et al.  Multiple Hypotheses Testing with Weights , 1997 .

[15]  J Zhang,et al.  Some statistical methods for multiple endpoints in clinical trials. , 1997, Controlled clinical trials.

[16]  L. Hothorn,et al.  Testing strategies in multi-dose experiments including active control. , 1998, Statistics in medicine.

[17]  Yosef Hochberg,et al.  Closed procedures are better and often admit a shortcut , 1999 .

[18]  P. Westfall,et al.  Optimally weighted, fixed sequence and gatekeeper multiple testing procedures , 2001 .

[19]  H. Hartung,et al.  Mitoxantrone in progressive multiple sclerosis: a placebo-controlled, double-blind, randomised, multicentre trial , 2002, The Lancet.

[20]  B. Wiens A fixed sequence Bonferroni procedure for testing multiple endpoints , 2003 .

[21]  P. Westfall,et al.  Gatekeeping strategies for clinical trials that do not require all primary effects to be significant , 2003, Statistics in medicine.

[22]  Hui Quan,et al.  Decision rule based multiplicity adjustment strategy , 2005, Clinical trials.

[23]  Xun Chen,et al.  The application of enhanced parallel gatekeeping strategies , 2005, Statistics in medicine.

[24]  H. Keselman,et al.  Multiple Comparison Procedures , 2005 .

[25]  Michael Hughes,et al.  Multiplicity in Clinical Trials , 2005 .