![]() ![]() For brevity, I will refer to this as the baseline risk. In fact, the relative risk depends also on the risk of the outcome in the baseline or control group. Not only is the odds ratio a poor approximation for outcomes which are not “rare” in the study, a statistical analysis with a single odds ratio, common to all participants, does not imply a single common relative risk. Medical papers sometimes rely too heavily on this approximation, discussing odds ratios in terms of risks. When the outcome is rare, the odds ratio and relative risk are about the same. Unfortunately, confusion about odds is not the only problem there is also a danger of inaccuracy when communicating odds ratios. This is a problem when communicating results to healthcare professionals and policy makers, discussing treatment options with patients, or seeking to conduct a meta-analysis of studies reporting effect sizes in a mixture of odds ratios and relative risks. Because the misunderstanding arises from the odds itself, simply describing it as a proportional change (for example, explaining an odds ratio of 0.8 as “treatment X was associated with a 20% reduction in the odds of the outcome”) is not helpful for most people. ![]() 1 2 3 The relative risk (also called the risk ratio) is more intuitive, but cannot be obtained from case-control studies or (except in rare instances) logistic regressions. However, the odds ratio is poorly understood. The odds ratio is a common measure in medical research of the effect size comparing two groups (treatments or risk factors) in terms of an outcome that is either present or absent. This paper provides practical advice for authors and readers on converting odds ratios to relative risks Odds ratios are a necessary evil in medical research although used as a measure of effect size from logistic regressions and case-control studies, they are poorly understood. ![]()
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