Using risk and odds ratios to assess effect size for meta-analysis outcome measures

Abstract

Best practice is built on the principle of aggregating all available evidence on a topic to make a clinical decision on the most appropriate intervention for the situation at hand. Systematic reviews and meta‐analyses are powerful tools that summarize the evidence for current best practice guidelines for the available interventions for a particular problem (Moher, Liberati, Tetzlaff, & Altman, 2009). Meta‐analysis combines the results of multiple studies to produce an aggregated and more precise estimates of the benefits of the interventions. Meta‐analysis of high‐quality randomized trials is considered the highest level of evidence to inform practice. When reading the healthcare literature, several measures of the effect of an intervention on an outcome are available to judge whether the evidence presented can be applied to clinical practice. It is important to be able to understand, correctly interpret, and honestly communicate these reported measures (Thapa, Visentin, Hunt, Watson, & Cleary, 2020). However, it is not uncommon for clinicians and researchers to be confused about the differences between the various effect measures available (Tufanaru, Munn, Stephenson, & Aromataris, 2015). These will be outlined further in this editorial, with a focus on the risk and odds ratios.