Towards better reconciling randomized controlled trial and observational study findings: Efficient algorithms for building representative matched samples with enhanced external
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Many recent efforts center on assessing the ability of real-world evidence (RWE) generated from nonrandomized observational data to provide results that are compatible with those from randomized controlled trials (RCTs). One noticeable endeavor is the RCT DUPLICATE initiative (Franklin et al., 2020). To better reconcile findings from observational and trial data, it is desirable to eliminate differences between the RCT and corresponding observational study populations. We outline an efficient, network-flow-based statistical matching algorithm that designs well-matched pairs from observational data that mimic the covariates' distribution of a target population, e.g., the RCT study population or a population of scientific interest. We demonstrate the usefulness of the method by revisiting the inconsistency regarding a cardioprotective effect of the hormone replacement therapy (HRT) in the Women's Health Initiative (WHI) clinical trial and corresponding observational study. We found that the discrepancy between the trial and observational study persisted in a design that adjusted for study populations' cardiovascular risk profile, but seemed to disappear in a study design that further adjusted for the HRT initiation age and previous estrogen-plus-progestin use. The proposed method is integrated into the R package match2C.
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