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A central goal of translational neuroimaging is to establish robust links between brain measures and clinical outcomes. Success hinges on the development of brain biomarkers with large effect sizes. With large enough effects, a measure may be diagnostic of outcomes at the individual patient level. Surprisingly, however, standard brain-mapping analyses are not designed to estimate or optimize the effect sizes of brain-outcome relationships, and estimates are often biased. Here, we review these issues and how to estimate effect sizes in neuroimaging research.
Effect size is a unit-free description of the strength of an effect, independent of sample size. Examples include Cohen d, Pearson r, and number needed to treat.1,2 For a given sample size (N), these can be converted to a t or z score (eg, Cohen d is t/[N]1/2). But t, z, F, and P values are sample size dependent and relate to the presence of an effect (statistical significance), not its magnitude. By contrast, effect size describes a finding’s practical significance, which determines its clinical importance. This is an important distinction because small effects can reach statistical significance given a large enough sample, even if they are unlikely to be of practical importance or replicable across diverse samples.3
Reddan MC, Lindquist MA, Wager TD. Effect Size Estimation in Neuroimaging. JAMA Psychiatry. 2017;74(3):207–208. doi:10.1001/jamapsychiatry.2016.3356
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