Converting effect measures to create common metrics for synthesis: A resource for conducting systematic reviews and meta-analyses

<ns5:p>Background Synthesising quantitative evidence in systematic reviews and meta-analyses often requires converting a range of reported effect measures into a common metric. This can be particularly challenging when reviews include non-randomised studies, where exposures and outcomes may be binary or continuous and reported in inconsistent ways. Existing resources such as the Cochrane Handbook and the Campbell Collaboration effect size calculator provide useful guidance, but do not cover all conversions reviewers encounter in practice. Methods We compiled and organised practical formulae for converting between commonly used effect measures, and for deriving standard errors from reported statistics. Methods are grouped by exposure–outcome combinations (binary–binary, binary–continuous, continuous–binary, and continuous–continuous). For each conversion we specify the required inputs, the relevant formula, and the underlying statistical assumptions, supplemented by worked examples and a signposting flow chart ( Figure 1). Results The resource summarises procedures for standardising effect sizes – including odds ratios, risk ratios, hazard ratios, standardised mean differences, and correlation-based estimates – and for calculating or back-calculating standard errors from confidence intervals, p-values, t-statistics, and sample sizes. Worked examples illustrate each conversion in context, and accompanying summaries make the underlying assumptions explicit so reviewers can judge when each conversion is appropriate. Conclusions This guide consolidates scattered methodological information into a single, accessible reference to support systematic reviewers – particularly those working with non-randomised studies – in synthesising data accurately and transparently. By clarifying common conversions and their assumptions, the resource aims to improve the quality, comparability, and reliability of meta-analytic evidence.</ns5:p>