Individual Patient Data Meta-Analysis

Aggregate meta-analysis combines published effect estimates (e.g., odds ratios) from multiple trials. IPD meta-analysis obtains raw patient-level data and re-analyzes it centrally with standardized methods. IPD allows consistent subgroup analyses and exploration of patient-treatment interactions; aggregate meta-analysis relies on subgroup results reported in papers, which are often inconsistent or missing. IPD is more rigorous but much more resource-intensive.

Missing data at the patient level (e.g., some patients missing an outcome measurement) is common and can be handled with imputation methods. Missing data at the trial level (entire trial not providing IPD) is more problematic. Aim for IPD from 60-80%+ of eligible trials. Document response rates and assess whether missing trials differ systematically (e.g., are negative trials less likely to respond?). If response is <50%, bias risk increases; sensitivity analyses become essential.

Include interaction terms in your mixed-effect regression model (e.g., treatment × age, treatment × baseline severity). Test the interaction term's statistical significance. Estimate the treatment effect separately for different covariate strata (e.g., age <65 vs ≥65). Plot the treatment effect across covariate ranges to visualize interactions. Pre-specify which interactions you will test before analyzing data to avoid false positives from multiple testing.

Yes, but clearly distinguish them and account for study design in analysis (e.g., use a mixed model including a study design indicator). RCTs provide strongest causal evidence; observational studies add breadth but carry confounding risk. Pre-specify whether observational studies will be analyzed separately, together with RCTs in a sensitivity analysis, or excluded entirely. Document any differences in findings by study design.