Propensity score methods for observational studies with clustered data: A review

Propensity score methods are a popular approach to mitigating confounding bias when estimating causal effects in observational studies. When study units are clustered (eg, patients nested within health systems), additional challenges arise such as accounting for unmeasured confounding at multiple levels and dependence between units within the same cluster. While clustered observational data are widely used to draw causal inferences in many fields, including medicine and healthcare, extensions of propensity score methods to clustered settings are still a relatively new area of research. This article presents a framework for estimating causal effects using propensity scores when study units are nested within clusters and are nonrandomly assigned to treatment conditions. We emphasize the need for investigators to examine the nature of the clustering, among other properties, of the observational data at hand in order to guide their choice of causal estimands and the corresponding propensity score approach.

Automated summary

Propensity score methods are commonly used to estimate causal effects in observational studies, but their application in clustered data is relatively new. This article presents a framework for estimating causal effects using propensity scores when study units are nested within clusters and are nonrandomly assigned to treatment conditions.