Propensity score weighting with survey weighted data when outcomes are binary: a simulation study.

Propensity score methods have been widely adopted in observational studies, however research on propensity score-based weighting (PSW) methods in complex survey data settings is lacking, particularly for binary outcomes. We conducted a simulation study to compare eight propensity score weighting approaches for estimating treatment effects using survey weighted data. Each of the eight methods is applied to estimation of two measures of the population-level treatment effect: the population average treatment effect (PATE), and the population average treatment effect on the treated (PATT). The methods are compared in terms of mean relative bias and coverage probability under different scenarios by varying the treatment effect, degrees of model misspecification, and levels of overlap in the propensity score. The results demonstrate that the two-stage methods with predicted outcomes weighted by survey weights consistently outperform the other methods for estimating the PATT; for estimating the PATE, the best performing PSW method depends on the degree of model misspecification and propensity score overlap. When the outcome model is correctly specified, four two-stage methods produce better estimates depending on the propensity score overlap. The methods are applied to the 2015 National Health Interview Survey data to estimate the effect of provider-patient discussion about smoking on smoking cessation.