Common pitfalls in statistical analysis: The perils of multiple testing

Priya Ranganathan; CS Pramesh; Marc Buyse

doi:10.4103/2229-3485.179436

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STATISTICS

Year : 2016 | Volume : 7 | Issue : 2 | Page : 106-107

Common pitfalls in statistical analysis: The perils of multiple testing

Priya Ranganathan¹, CS Pramesh², Marc Buyse³
¹ Department of Anaesthesiology, Tata Memorial Centre, Mumbai, Maharashtra, India
² Department of Surgical Oncology, Division of Thoracic Surgery, Tata Memorial Centre, Mumbai, Maharashtra, India
³ International Drug Development Institute, San Francisco, California, USA; Department of Biostatistics, Hasselt University, Hasselt, Belgium

Correspondence Address:
Priya Ranganathan
Department of Anaesthesiology, Tata Memorial Centre, Ernest Borges Road, Parel, Mumbai - 400 012, Maharashtra
India

Source of Support: None, Conflict of Interest: None

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DOI: 10.4103/2229-3485.179436

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.

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