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Utilizing Propensity Scores to Estimate Causal Treatment Effects with Censored Time‐Lagged Data
Author(s) -
Anstrom Kevin J.,
Tsiatis Anastasios A.
Publication year - 2001
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.0006-341x.2001.01207.x
Subject(s) - censoring (clinical trials) , estimator , observational study , propensity score matching , inverse probability , statistics , covariate , mathematics , average treatment effect , confounding , econometrics , delta method , medicine , bayesian probability , posterior probability
Summary. Observational studies frequently are conducted to compare long‐term effects of treatments. Without randomization, patients receiving one treatment are not guaranteed to be prognostically comparable to those receiving another treatment. Furthermore, the response of interest may be right‐censored because of incomplete follow‐up. Statistical methods that do not account for censoring and confounding may lead to biased estimates. This article presents a method for estimating treatment effects in nonrandomized studies with right‐censored responses. We review the assumptions required to estimate average causal effects and derive an estimator for comparing two treatments by applying inverse weights to the complete cases. The weights are determined according to the estimated probability of receiving treatment conditional on covariates and the estimated treatment‐specific censoring distribution. By utilizing martingale representations, the estimator is shown to be asymptotically normal and an estimator for the asymptotic variance is derived. Simulation results are presented to evaluate the properties of the estimator. These methods are applied to an observational data set of acute coronary syndrome patients from Duke University Medical Center to estimate the effect of a treatment strategy on the mean 5‐year medical cost.