Title | On variance estimate for covariate adjustment by propensity score analysis. |
Publication Type | Journal Article |
Year of Publication | 2016 |
Authors | Zou, Baiming, Fei Zou, Jonathan J. Shuster, Patrick J. Tighe, Gary G. Koch, and Haibo Zhou |
Journal | Stat Med |
Volume | 35 |
Issue | 20 |
Pagination | 3537-48 |
Date Published | 2016 Sep 10 |
ISSN | 1097-0258 |
Keywords | Bias, 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. |
DOI | 10.1002/sim.6943 |
Alternate Journal | Stat Med |
Original Publication | On variance estimate for covariate adjustment by propensity score analysis. |
PubMed ID | 26999553 |
PubMed Central ID | PMC4961520 |
Grant List | R01 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 |
On variance estimate for covariate adjustment by propensity score analysis.
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