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Huque Dmitrienko DAgostino 2013

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Huque, M.F., Dmitrienko, A., D'Agostino, R.B. (2013). Multiplicity issues in clinical trials with multiple objectives. Statistics in Biopharmaceutical Research. 5, 321-337.

Abstract

Modern clinical trials for evaluating efficacy and safety of new treatments frequently include multiple objectives with questions of varying clinical importance. Answering them generally requires performing a number of statistical tests and analyses which raise multiplicity of tests issues. These issues can be complex and multi-dimensional in nature. For example, one dimension may relate to the assessment of the effects of the treatment on multiple endpoints, the other to the effects of multiple doses of the treatment, and yet another to the type of the tests (e.g., superiority or non-inferiority type tests). Also, the trial may seek claims for the treatment benefits either for the total patient population or for targeted subgroups. In addition, there may be interests in finding whether or not certain consistency of results persists across certain multiple endpoints which in some situations may be measuring different but critical features of a disorder, or in other situations, may be measuring the same underlying pathophysiology of the disorder. Addressing such problems of clinical trials for the purpose of controlling the Type I error rate requires the use of advanced statistical test strategies and methods some of which have appeared only in recent publications. Actually, the last decade has witnessed a number of novel methods as well as innovative extensions of old methods for addressing complex multiplicity problems of clinical trials. The main purpose of this paper is to present at the conceptual level how multiplicity issues of confirmatory clinical trials that include multiple objectives can be addressed by using some of these new statistical methods that use α-propagation and gatekeeping concepts. Additional purpose is to address some issues that often arise in the use of co-primary and composite endpoints in clinical trials.

SAS code

Download SAS code (sample size calculations discussed in Section 7).

Review papers and chapters

For more information on multiple testing procedures in clinical trials, see