Population-level and individual-level explainers for propensity score matching in observational studies.

ABSTRACT:

Precis

The exclusion of unmatched observations in propensity score matching has implications for the generalizability of causal effects. Machine learning methods can help to identify how the study population differs from the unmatched subpopulation.

Background

There has been widespread use of propensity scores in evaluating the effect of cancer treatments on survival, particularly in administrative databases and cancer registries. A byproduct of certain matching schemes is the exclusion of observations. Borrowing an analogy from clinical trials, one can view these exclusions as subjects that do not satisfy eligibility criteria.

Methods

Developing identification rules for these "data-driven eligibility criteria" in observational studies on both population and individua

SUBMITTER: Ghosh D 

PROVIDER: S-EPMC9630947 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature