Abstract
Multiplicity considerations play an important role in clinical trials with multiple treatment comparisons or endpoints. This article provides a high-level review of key concepts in multiple testing, including control of false-positive outcomes and popular multiplicity adjustments. The article also introduces and compares important statistical principles (union-intersection, closed testing and partitioning principles) which provide a foundation for widely used multiple tests. The statistical methods described in this article are illustrated using examples from clinical trials. Keywords: multiple comparisons; Type I error rate; familywise error rate; union-intersection testing; closed testing; partition testing; adjusted p-values
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