Utilizing propensity scores to estimate causal treatment effects with censored time-lagged data.


Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Anstrom, KJ; Tsiatis, AA

Published Date

  • December 2001

Published In

Volume / Issue

  • 57 / 4

Start / End Page

  • 1207 - 1218

PubMed ID

  • 11764262

Pubmed Central ID

  • 11764262

International Standard Serial Number (ISSN)

  • 0006-341X


  • eng

Conference Location

  • United States