Efficient and Robust Propensity-Score-Based Methods for Finite Population Inference with Nonprobability Epidemiologic Cohorts

• Activity Number: 310 - Making Finite Population Inferences from Nonprobability Samples
• Type: Topic-Contributed
• Date/Time: Wednesday, August 11, 2021 : 3:30 PM to 5:20 PM
• Sponsor: Survey Research Methods Section
• Author(s): Lingxiao Wang* and Yan Li and Barry Graubard and Hormuzd Katki
• Companies: National Cancer Institute, DCEG, Biostatistics Branch and University of Maryland, College Park and National Cancer Institute, DCEG, Biostatistics Branch and National Cancer Institute
• Keywords: Epidemiologic cohort; Non-Probability sample; Propensity score weighting; Survey Sampling; Taylor series linearization variance

Abstract

Most epidemiologic cohorts are composed of volunteers who do not represent the general population. To enable population inference from cohorts, we develop a unified framework for propensity score (PS)-based weighting (such as inverse PS weighting (IPSW)) and matching methods (such as kernel-weighting (KW) method) by utilizing probability survey samples as external references. 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.