Propensity Score Weighting to Ensure Balance in Key Subgroups or Strata: A Practical Guide

TL;DR

This paper provides practical guidance on implementing propensity score weighting with stratification by clinical subgroups to improve causal inference in complex healthcare datasets.

Contribution

It introduces a stratified propensity score weighting approach tailored for heterogeneous patient populations and offers best practices for EHR and administrative data analysis.

Findings

Stratification improves balance in key patient subgroups.

Guidance on handling covariate-subgroup interactions.

Applicable to large healthcare datasets and population health studies.

Abstract

Propensity score weighting approaches have been widely implemented in clinical research to estimate the effects of a treatment or exposure while mitigating the risk of confounding in the absence of random assignment. In practice, when working with large electronic health records (EHR) or administrative datasets to evaluate health quality outcomes at the institutional level, or evaluate supportive care interventions for a wide range of hospitalized patients, it may be advisable to stratify the propensity score weighting approach by indication, reason for admission, or other clinical risk factors due to the potential for substantial heterogeneity across subgroups of patients with complex care needs. A stratified approach may be appropriate if (i) prognosis differs substantially between patient subgroups such that achieving balance in the composition of these strata between exposure/treatment groups should be prioritized, (ii) likelihood of exposure differs substantially across clinical subgroups, or (iii) the covariate-exposure associations are expected to differ substantially between subgroups (i.e. there are covariate-subgroup interactions in the exposure/treatment propensity model). For example, we may want to evaluate the impact of prophylactic anticoagulant use for venous thromboembolism prevention in elderly patients admitted to hospital for a wide array of conditions. The purpose of this article is to outline an approach to implementing propensity score weighting with stratification by clinical groups. We also provide guidance on best practices with particular focus on EHR and administrative medical data, and population health settings.