title: Common pitfalls in statistical analysis: The perils of multiple testing.
abstract: Multiple testing refers to situations where a dataset is subjected to statistical testing multiple times - either at multiple time-points or through multiple subgroups or for multiple end-points. This amplifies the probability of a false-positive finding. In this article, we look at the consequences of multiple testing and explore various methods to deal with this issue.
authors.affiliation: Department of Anaesthesiology, Tata Memorial Centre, Mumbai, Maharashtra, India.
authors.forename: Priya
authors.initials: P
authors.lastname: Ranganathan
authors.affiliation: Department of Surgical Oncology, Division of Thoracic Surgery, Tata Memorial Centre, Mumbai, Maharashtra, India.
authors.forename: C S
authors.initials: CS
authors.lastname: Pramesh
authors.affiliation: International Drug Development Institute, San Francisco, California, USA; Department of Biostatistics, Hasselt University, Hasselt, Belgium, USA.
authors.forename: Marc
authors.initials: M
authors.lastname: Buyse
keywords: Biostatistics; data interpretation; multiplicity; statistical significance