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Variance estimation when using inverse probability of treatment weighting (IPTW) with survival analysis.
Stat Med. 2016 12 30; 35(30):5642-5655.SM

Abstract

Propensity score methods are used to reduce the effects of observed confounding when using observational data to estimate the effects of treatments or exposures. A popular method of using the propensity score is inverse probability of treatment weighting (IPTW). When using this method, a weight is calculated for each subject that is equal to the inverse of the probability of receiving the treatment that was actually received. These weights are then incorporated into the analyses to minimize the effects of observed confounding. Previous research has found that these methods result in unbiased estimation when estimating the effect of treatment on survival outcomes. However, conventional methods of variance estimation were shown to result in biased estimates of standard error. In this study, we conducted an extensive set of Monte Carlo simulations to examine different methods of variance estimation when using a weighted Cox proportional hazards model to estimate the effect of treatment. We considered three variance estimation methods: (i) a naïve model-based variance estimator; (ii) a robust sandwich-type variance estimator; and (iii) a bootstrap variance estimator. We considered estimation of both the average treatment effect and the average treatment effect in the treated. We found that the use of a bootstrap estimator resulted in approximately correct estimates of standard errors and confidence intervals with the correct coverage rates. The other estimators resulted in biased estimates of standard errors and confidence intervals with incorrect coverage rates. Our simulations were informed by a case study examining the effect of statin prescribing on mortality. © 2016 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.

Authors+Show Affiliations

Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada. Institute of Health Management, Policy and Evaluation, University of Toronto, Toronto, Ontario, Canada. Schulich Heart Research Program, Sunnybrook Research Institute, Toronto, Canada.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

27549016

Citation

Austin, Peter C.. "Variance Estimation when Using Inverse Probability of Treatment Weighting (IPTW) With Survival Analysis." Statistics in Medicine, vol. 35, no. 30, 2016, pp. 5642-5655.
Austin PC. Variance estimation when using inverse probability of treatment weighting (IPTW) with survival analysis. Stat Med. 2016;35(30):5642-5655.
Austin, P. C. (2016). Variance estimation when using inverse probability of treatment weighting (IPTW) with survival analysis. Statistics in Medicine, 35(30), 5642-5655. https://doi.org/10.1002/sim.7084
Austin PC. Variance Estimation when Using Inverse Probability of Treatment Weighting (IPTW) With Survival Analysis. Stat Med. 2016 12 30;35(30):5642-5655. PubMed PMID: 27549016.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Variance estimation when using inverse probability of treatment weighting (IPTW) with survival analysis. A1 - Austin,Peter C, Y1 - 2016/08/22/ PY - 2016/03/09/received PY - 2016/06/09/revised PY - 2016/08/01/accepted PY - 2016/8/24/pubmed PY - 2018/2/24/medline PY - 2016/8/24/entrez KW - Monte Carlo simulations KW - inverse probability of treatment weighting (IPTW) KW - observational study KW - propensity score KW - survival analysis KW - variance estimation SP - 5642 EP - 5655 JF - Statistics in medicine JO - Stat Med VL - 35 IS - 30 N2 - Propensity score methods are used to reduce the effects of observed confounding when using observational data to estimate the effects of treatments or exposures. A popular method of using the propensity score is inverse probability of treatment weighting (IPTW). When using this method, a weight is calculated for each subject that is equal to the inverse of the probability of receiving the treatment that was actually received. These weights are then incorporated into the analyses to minimize the effects of observed confounding. Previous research has found that these methods result in unbiased estimation when estimating the effect of treatment on survival outcomes. However, conventional methods of variance estimation were shown to result in biased estimates of standard error. In this study, we conducted an extensive set of Monte Carlo simulations to examine different methods of variance estimation when using a weighted Cox proportional hazards model to estimate the effect of treatment. We considered three variance estimation methods: (i) a naïve model-based variance estimator; (ii) a robust sandwich-type variance estimator; and (iii) a bootstrap variance estimator. We considered estimation of both the average treatment effect and the average treatment effect in the treated. We found that the use of a bootstrap estimator resulted in approximately correct estimates of standard errors and confidence intervals with the correct coverage rates. The other estimators resulted in biased estimates of standard errors and confidence intervals with incorrect coverage rates. Our simulations were informed by a case study examining the effect of statin prescribing on mortality. © 2016 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd. SN - 1097-0258 UR - https://www.unboundmedicine.com/medline/citation/27549016/Variance_estimation_when_using_inverse_probability_of_treatment_weighting__IPTW__with_survival_analysis_ L2 - https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/27549016/ DB - PRIME DP - Unbound Medicine ER -