Emagnification: A tool for estimating effect-size magnification and performing design calculations in epidemiological studies. (September 2020)

• Record Type: Journal Article
• Authors: Miller, David J.; Nguyen, James T.; Bottai, Matteo
• Is Part Of: Stata journal. Volume 20:Number 3(2020)
• Page Start: 548
• Page End: 564
• Publication Date: 2020-09
• Subjects: st0608 -- emagnification proportion -- emagnification rate -- inflation -- magnification -- p-value -- type M error -- effect-size magnification -- winners curse; Statistics -- Periodicals; Statistics -- Computer programs -- Periodicals; 001.422
• DOI: 10.1177/1536867X20953567
• Languages: English
• ISSNs: 1536-867X

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 Gelman 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 calledemagnification that allows straightforward implementation of such simulations in Stata. The commands automate these simulations 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.