@article{ref1,
title="Estimating the size of treatment effects: Moving beyond P values",
journal="Psychiatry (Edgmont)",
year="2009",
author="McGough, J.J. and Faraone, S.V.",
volume="6",
number="10",
pages="21-29",
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. <br><br>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. <br><br>CONCLUSION: Effect size statistics provide a better estimate of treatment effects than P values alone.<p /><p>Language: en</p>",
language="en",
issn="1550-5952",
doi="",
url="http://dx.doi.org/"
}