emagnification: A tool for estimating effect-size magnification and performing design calculations in epidemiological studies

David J. Miller (), James T. Nguyen () and Matteo Bottai ()
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David J. Miller: U.S. Environmental Protection Agency
James T. Nguyen: U.S. Environmental Protection Agency
Matteo Bottai: Institute of Environmental Medicine, Karolinska Institutet

Stata Journal, 2020, vol. 20, issue 3, 548-564

Abstract: Artificial effect-size magnification (ESM) may occur in underpowered studies, where effects are reported only because they or their associated p-values have passed some threshold. Ioannidis (2008, Epidemiology 19: 640–648) and Gel- man and Carlin (2014, Perspectives on Psychological Science 9: 641–651) have suggested that the plausibility of findings for a specific study can be evaluated by computation of ESM, which requires statistical simulation. In this article, we present a new command called emagnification that allows straightforward im- plementation of such simulations in Stata. The commands automate these sim- ulations for epidemiological studies and enable the user to assess ESM routinely for published studies using user-selected, study-specific inputs that are commonly reported in published literature. The intention of the command is to allow a wider community to use ESMs as a tool for evaluating the reliability of reported effect sizes and to put an observed statistically significant effect size into a fuller context with respect to potential implications for study conclusions.

Keywords: emagnification proportion; emagnification rate; inflation; mag- nification; p-value; type M error; effect-size magnification; winners curse (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1177/1536867X20953567

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