Choosing effect measures in Axelium
Effect measures determine how study results are represented and pooled. Axelium suggests defaults by endpoint type, and you can override them when your protocol requires a different choice.
Binary outcomes
For outcomes that represent occurrence vs. non-occurrence of an event:
- Risk Ratio (RR) — intuitive for relative risk comparisons; preferred for cohort and RCT designs where absolute risks are estimable.
- Odds Ratio (OR) — common in case-control and logistic regression models; natural output when absolute event rates are not directly available.
- Hazard Ratio (HR) — for time-to-event endpoints where censoring occurs; used in survival analysis contexts.
Continuous outcomes
For outcomes measured on a numeric scale:
- Mean Difference (MD) — used when all studies report the same units; preserves clinical interpretability.
- Standardized Mean Difference (SMD) — used when studies measure the same construct on different scales; expressed in standard deviation units.
Single-group prevalence
For prevalence and incidence estimates without a comparator:
- Use pooled proportions with appropriate transformations (logit, Freeman-Tukey double arcsine) depending on whether extreme values are expected.
- Random-effects assumptions are usually appropriate when pooling prevalence across heterogeneous populations.
Practical selection checklist
- Match the measure to the endpoint definition and study design.
- Keep the measure consistent with your protocol registration.
- Document overrides and rationale in your audit trail.
- Consider clinical interpretability when choosing between RR and OR — when event rates differ substantially across populations, RRs are generally more informative.
