Relative effect sizes for measures of risk

Jake Olivier, Warren L. May, Melanie L. Bell

Research output: Contribution to journalArticlepeer-review

123 Scopus citations


Effect sizes are an important component of experimental design, data analysis, and interpretation of statistical results. In some situations, an effect size of clinical or practical importance may be unknown to the researcher. In other situations, the researcher may be interested in comparing observed effect sizes to known standards to quantify clinical importance. In these cases, the notion of relative effect sizes (small, medium, large) can be useful as benchmarks. Although there is generally an extensive literature on relative effect sizes for continuous data, little of this research has focused on relative effect sizes for measures of risk that are common in epidemiological or biomedical studies. The aim of this paper, therefore, is to extend existing relative effect sizes to the relative risk, odds ratio, hazard ratio, rate ratio, and Mantel–Haenszel odds ratio for related samples. In most scenarios with equal group allocation, effect sizes of 1.22, 1.86, and 3.00 can be taken as small, medium, and large, respectively. The odds ratio for a non rare event is a notable exception and modified relative effect sizes are 1.32, 2.38, and 4.70 in that situation.

Original languageEnglish (US)
Pages (from-to)6774-6781
Number of pages8
JournalCommunications in Statistics - Theory and Methods
Issue number14
StatePublished - Jul 18 2017


  • Effect size
  • epidemiology
  • odds ratio
  • relative risk
  • risk measures

ASJC Scopus subject areas

  • Statistics and Probability


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