Abstract
The confirmatory analysis of pre-specified multiple hypotheses has become common in pivotal clinical trials. In the recent past multiple test procedures have been developed that reflect the relative importance of different study objectives, such as fixed sequence, fallback, and gatekeeping procedures. In addition, graphical approaches have been proposed that facilitate the visualization and communication of Bonferroni-based closed test procedures for common multiple test problems, such as comparing several treatments with a control, assessing the benefit of a new drug for more than one endpoint, combined non-inferiority and superiority testing, or testing a treatment at different dose levels in an overall and a subpopulation. In this paper, we focus on extended graphical approaches by dissociating the underlying weighting strategy from the employed test procedure. This allows one to first derive suitable weighting strategies that reflect the given study objectives and subsequently apply appropriate test procedures, such as weighted Bonferroni tests, weighted parametric tests accounting for the correlation between the test statistics, or weighted Simes tests. We illustrate the extended graphical approaches with several examples. In addition, we describe briefly the gMCP package in R, which implements some of the methods described in this paper.
Highlights
Multiple test procedures are often used in the analysis of clinical trials addressing multiple objectives, such as comparing several treatments with a control and assessing the benefit of a new drug for more than one endpoint
We focus on graphical approaches which have been introduced independently by Bretz et al (2009) and Burman et al (2009)
While the original graphical approaches were introduced based on weighted Bonferroni tests, we propose here to dissociate the underlying weighting strategy from the employed test procedure
Summary
Multiple test procedures are often used in the analysis of clinical trials addressing multiple objectives, such as comparing several treatments with a control and assessing the benefit of a new drug for more than one endpoint. Li and Mehrotra (2008) introduced a more general approach for adapting the significance level to test secondary hypotheses based on the. Alosh and Huque (2009) introduced the notion of consistency when testing for an effect in the overall population and in a specific subgroup The authors extended this consistency concept to other situations (Alosh and Huque, 2010), including how to address multiplicity issues of a composite endpoint and its components in clinical trials (Huque et al, 2011). One can explore different test strategies together with the clinical team and tailor the multiple test procedure to the given study objectives.
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