title: On variance estimate for covariate adjustment by propensity score analysis.
abstract: Propensity score (PS) methods have been used extensively to adjust for confounding factors in the statistical analysis of observational data in comparative effectiveness research. There are four major PS-based adjustment approaches: PS matching, PS stratification, covariate adjustment by PS, and PS-based inverse probability weighting. Though covariate adjustment by PS is one of the most frequently used PS-based methods in clinical research, the conventional variance estimation of the treatment effects estimate under covariate adjustment by PS is biased. As Stampf et al. have shown, this bias in variance estimation is likely to lead to invalid statistical inference and could result in erroneous public health conclusions (e.g., food and drug safety and adverse events surveillance). To address this issue, we propose a two-stage analytic procedure to develop a valid variance estimator for the covariate adjustment by PS analysis strategy. We also carry out a simple empirical bootstrap resampling scheme. Both proposed procedures are implemented in an R function for public use. Extensive simulation results demonstrate the bias in the conventional variance estimator and show that both proposed variance estimators offer valid estimates for the true variance, and they are robust to complex confounding structures. The proposed methods are illustrated for a post-surgery pain study. Copyright © 2016 John Wiley & Sons, Ltd.
authors.affiliation: Department of Biostatistics, University of Florida, Gainesville, 32611, FL, U.S.A.
authors.forename: Baiming
authors.initials: B
authors.lastname: Zou
authors.affiliation: Department of Biostatistics, University of North Carolina - Chapel Hill, Chapel Hill, NC, 27599, U.S.A.
authors.forename: Fei
authors.initials: F
authors.lastname: Zou
authors.affiliation: Department of Health Outcomes and Policy, University of Florida, Gainesville, FL, 32611, U.S.A.
authors.forename: Jonathan J
authors.initials: JJ
authors.lastname: Shuster
authors.affiliation: Department of Anesthesiology, University of Florida, Gainesville, FL, 32611, U.S.A.
authors.forename: Patrick J
authors.initials: PJ
authors.lastname: Tighe
authors.affiliation: Department of Biostatistics, University of North Carolina - Chapel Hill, Chapel Hill, NC, 27599, U.S.A.
authors.forename: Gary G
authors.initials: GG
authors.lastname: Koch
authors.affiliation: Department of Biostatistics, University of North Carolina - Chapel Hill, Chapel Hill, NC, 27599, U.S.A.
authors.forename: Haibo
authors.initials: H
authors.lastname: Zhou
mesh_terms: D015982:Bias; D015986:Confounding Factors, Epidemiologic; D006801:Humans; D064887:Observational Studies as Topic; D057216:Propensity Score
keywords: Bootstrap; comparative effectiveness research; confounding factors; joint likelihood; propensity score; two-stage regression