Efficient and Robust Propensity-Score-Based Methods for Population Inference using Epidemiologic Cohorts

TL;DR

This paper introduces a unified framework for propensity score methods in epidemiologic cohort analysis, relaxing key assumptions and reducing estimator variance to improve population inference accuracy.

Contribution

It develops new IPSW.S and KW.S methods that relax exchangeability assumptions and lower variance, with proven consistency and superior performance in simulations and real data.

Findings

KW.S and IPSW.S estimators have smallest MSE in simulations

Original KW estimates exhibited large bias in data example

Proposed methods improve inference robustness and efficiency

Abstract

Most epidemiologic cohorts are composed of volunteers who do not represent the general population. To enable population inference from cohorts, we and others have proposed utilizing probability survey samples as external references to develop a propensity score (PS) for membership in the cohort versus survey. Herein we develop a unified framework for PS-based weighting (such as inverse PS weighting (IPSW)) and matching methods (such as kernel-weighting (KW) method). We identify a fundamental Strong Exchangeability Assumption (SEA) underlying existing PS-based matching methods whose failure invalidates inference even if the PS-model is correctly specified. We relax the SEA to a Weak Exchangeability Assumption (WEA) for the matching method. Also, we propose IPSW.S and KW.S methods that reduce the variance of PS-based estimators by scaling the survey weights used in the PS estimation. We prove consistency of the IPSW.S and KW.S estimators of population means and prevalences under WEA, and provide asymptotic variances and consistent variance estimators. In simulations, the KW.S and IPSW.S estimators had smallest MSE. In our data example, the original KW estimates had large bias, whereas the KW.S estimates had the smallest MSE.