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Estimating the size of treatment effects: moving beyond p values
James J McGough1, Stephen V Faraone
1Dr. McGough is Professor of Clinical Psychiatry at the Semel Institute for Neuroscience and Human Behavior and David Geffen School of Medicine at the University of California, Los Angeles.
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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