• Mirjam J Knol.
• UMC Utrecht, Julius Centrum voor Gezondheidswetenschappen en Eerstelijns Geneeskunde, Utrecht en RIVM, Centrum voor Infectieziektebestrijding, Bilthoven, the Netherlands. mirjam.knol@rivm.nl
• Ned Tijdschr Geneeskd. 2012 Jan 1; 156 (28): A4775.

AbstractVarious effect measures are available for quantifying the relationship between an intervention or a risk factor and an outcome, such as the risk ratio and the odds ratio. Odds ratios are intended for use in case-control studies in which they are an appropriate measure for estimating the relative risk; however, this measure is also often presented in cohort studies and in randomized clinical trials. When used for cohort studies and randomized clinical trials, the odds ratio is often incorrectly interpreted as the risk ratio; the odds ratio then provides an overestimation of the risk ratio, especially when the outcome is frequent. The use of logistic regression to adjust for confounding is one of the reasons that odds ratios are presented. For cohort studies and randomized clinical trials, however, there are methods to estimate adjusted risk ratios; these include the Mantel-Haenszel method, log-binomial regression, Poisson regression with robust standard error, and 'doubling of cases' method with robust standard error. To avoid misinterpretation of odds ratios, risk ratios should be calculated in cohort studies and randomized clinical trials.

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