  Many recent efforts center on assessing the ability of real-world evidence
(RWE) generated from non-randomized, observational data to produce results
compatible with those from randomized controlled trials (RCTs). One noticeable
endeavor is the RCT DUPLICATE initiative (Franklin et al., 2020, 2021). To
better reconcile findings from an observational study and an RCT, or two
observational studies based on different databases, it is desirable to
eliminate differences between study populations. We outline an efficient,
network-flow-based statistical matching algorithm that designs well-matched
pairs from observational data that resemble the covariate distributions of a
target population, for instance, the target-RCT-eligible population in the RCT
DUPLICATE initiative studies or a generic 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.
