Meta-analysis with sample-standardization in multi-site studies

Purpose To conceptualize a particular target population and estimand for multi-site pharmacoepidemiologic studies within data networks and to analytically examine sample-standardization as a meta-analytic method compared with inverse-variance weighted meta-analyses. Methods The target population of interest is all and only all individuals from the data-contributing sites. Standardization, a general conditioning technique frequently employed for confounding control, was adopted to estimate the network-wide causal treatment effect. Specifically, the proposed sample-standardization yields a meta-analysis estimator, that is, a weighted summation of site-specific results, where the weight for a site is the proportion of its size in the entire network. This sample-standardization estimator was evaluated analytically in comparison to estimators from inverse-variance weighted fixed-effect and random-effects meta-analyses in terms of statistical consistency. Results A proof is reported to justify the consistency of the sample-standardization estimator with and without treatment effect heterogeneity by site. Both inverse-variance weighted fixed-effect and random-effects meta-analyses were found to generally result in inconsistent estimators in the presence of treatment effect heterogeneity by site for this particular target population and estimand. Conclusions Sample-standardization is a valid approach to generate causal inference in multi-site studies when the target population comprises all and only all individuals within the network, even in the presence of heterogeneity of treatment effect by site. Multi-site studies should clearly specify the target population and estimand to help select the most appropriate meta-analytic methods.

Automated summary

The purpose of this study is to conceptualize a specific target population and estimand for multi-site pharmacoepidemiologic studies within data networks and to compare sample-standardization as a meta-analytic method with inverse-variance weighted meta-analyses. The results suggest that sample-standardization is a valid approach for generating causal inference in multi-site studies, even in the presence of treatment effect heterogeneity by site. It is recommended that multi-site studies clearly specify the target population and estimand to select the most appropriate meta-analytic method.