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Most epidemiologic cohorts are composed of volunteers who do not representthe general population. To enable population inference from cohorts, we andothers have proposed utilizing probability survey samples as externalreferences to develop a propensity score (PS) for membership in the cohortversus survey. Herein we develop a unified framework for PS-based weighting(such as inverse PS weighting (IPSW)) and matching methods (such askernel-weighting (KW) method). We identify a fundamental Strong ExchangeabilityAssumption (SEA) underlying existing PS-based matching methods whose failureinvalidates inference even if the PS-model is correctly specified. We relax theSEA 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-basedestimators by scaling the survey weights used in the PS estimation. We proveconsistency of the IPSW.S and KW.S estimators of population means andprevalences under WEA, and provide asymptotic variances and consistent varianceestimators. In simulations, the KW.S and IPSW.S estimators had smallest MSE. Inour data example, the original KW estimates had large bias, whereas the KW.Sestimates had the smallest MSE.

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