Choosing effect measures

Binary outcomes

• Risk ratio. Useful when event risks are interpretable and studies report events and totals.
• Odds ratio. Common for case-control designs and logistic models; interpret carefully when events are common.
• Risk difference. Useful when absolute effects are important and baseline risks are comparable. Computed and pooled directly from reported events and totals, like risk ratios — including studies with zero events in an arm. Results stay on the absolute scale, where a difference of 0 means no effect.

Continuous outcomes

• Mean difference. Use when all studies measure the outcome on the same scale.
• Standardised mean difference. Use when studies measure the same construct on different scales.
• Change scores. Document whether you analyse final values, change from baseline, or adjusted differences.

Time-to-event outcomes

Hazard ratios are appropriate when follow-up time and censoring matter. Do not mix hazard ratios with simple binary events unless the protocol has a defensible transformation or hierarchy for doing so.

Prevalence and single-group outcomes

Prevalence, incidence, count, and single-arm mean outcomes often need different transformations and interpretation from comparative effects. Use random-effects models when heterogeneity across populations is expected, and report prediction intervals when they are meaningful.

Network meta-analysis measure scope

NMA requires compatible comparative effects across a connected set of treatments. Keep the NMA measure set narrower than the full pairwise catalogue unless your data, assumptions, and protocol justify the broader choice. Axelium's NMA workflow is centred on clinical comparative outcomes with reviewer-approved treatment definitions.

Ecology and evolution measures

Ecology/evolution reviews can use the log response ratio (lnRR), the standardised mean difference (Hedges' g), the Fisher z-transformed correlation, the log coefficient of variation ratio (lnCVR), the log variability ratio (lnVR), or a generic inverse-variance estimate for a protocol-defined effect. These are computed from arm-level means, standard deviations, and sample sizes (or from correlations), or supplied pre-computed. Analysis runs on the transformed scale and results are back-transformed for reporting: response and variability ratios return to the ratio scale (null of 1), and correlations return via the inverse Fisher transform (null of 0). These measures often depend on clear moderator, dependency, and covariance assumptions, especially when one study contributes multiple effects.

RevMan export notes

RevMan export supports the common Cochrane handoff for included and excluded studies plus supported quantitative outcomes. Single-arm, qualitative, ecology-specific, and unsupported measures may be listed as notes rather than modelled as RevMan comparisons. Risk-of-bias assessment exists in Axelium, but the current RevMan quality export is limited; check RevMan-specific quality fields after import.

Practical checklist

• Pre-specify the measure in the protocol when possible.
• Keep direction of benefit consistent across studies.
• Do not combine adjusted and unadjusted estimates without a rationale.
• Check that transformed estimates remain interpretable to the target audience.
• Document any override from the suggested measure.

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