On variance estimate for covariate adjustment by propensity score analysis.

TitleOn variance estimate for covariate adjustment by propensity score analysis.
Publication TypeJournal Article
Year of Publication2016
AuthorsZou, Baiming, Fei Zou, Jonathan J. Shuster, Patrick J. Tighe, Gary G. Koch, and Haibo Zhou
JournalStat Med
Volume35
Issue20
Pagination3537-48
Date Published2016 09 10
ISSN1097-0258
KeywordsBias, Confounding Factors, Epidemiologic, Humans, Observational Studies as Topic, Propensity Score
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.

DOI10.1002/sim.6943
Alternate JournalStat Med
Original PublicationOn variance estimate for covariate adjustment by propensity score analysis.
PubMed ID26999553
PubMed Central IDPMC4961520
Grant ListR01 GM114290 / GM / NIGMS NIH HHS / United States
UL1 TR000064 / TR / NCATS NIH HHS / United States
UL1 TR001427 / TR / NCATS NIH HHS / United States
P01 CA142538 / CA / NCI NIH HHS / United States
K23 GM102697 / GM / NIGMS NIH HHS / United States
R01 ES021900 / ES / NIEHS NIH HHS / United States