Estimating the size of treatment effects: moving beyond p values.

Abstract

OBJECTIVE: To increase understanding of effect size calculations among clinicians who over-rely on interpretations of P values in their assessment of the medical literature. DESIGN: We review five methods of calculating effect sizes: Cohen's d (also known as the standardized mean difference)-used in studies that report efficacy in terms of a continuous measurement and calculated from two mean values and their standard deviations; relative risk-the ratio of patients responding to treatment divided by the ratio of patients responding to a different treatment (or placebo), which is particularly useful in prospective clinical trials to assess differences between treatments; odds ratio- used to interpret results of retrospective case-control studies and provide estimates of the risk of side effects by comparing the probability (odds) of an outcome occurring in the presence or absence of a specified condition; number needed to treat-the number of subjects one would expect to treat with agent A to have one more success (or one less failure) than if the same number were treated with agent B; and area under the curve (also known as the drug-placebo response curve)-a six-step process that can be used to assess the effects of medication on both worsening and improvement and the probability that a medication-treated subject will have a better outcome than a placebo-treated subject. CONCLUSION: Effect size statistics provide a better estimate of treatment effects than P values alone.

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