Journal
BIOMETRICS
Volume 67, Issue 3, Pages 740-749Publisher
WILEY
DOI: 10.1111/j.1541-0420.2010.01503.x
Keywords
Counterfactual; Cumulative treatment effect; Inverse weighting; Proportional hazards model
Funding
- National Institutes of Health [2R01-DK070869]
- Health Resources and Services Administration (HRSA), U.S. Department of Health and Human Services
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In epidemiologic studies of time to an event, mean lifetime is often of direct interest. We propose methods to estimate group- (e.g., treatment-) specific differences in restricted mean lifetime for studies where treatment is not randomized and lifetimes are subject to both dependent and independent censoring. The proposed methods may be viewed as a hybrid of two general approaches to accounting for confounders. Specifically, treatment-specific proportional hazards models are employed to account for baseline covariates, while inverse probability of censoring weighting is used to accommodate time-dependent predictors of censoring. The average causal effect is then obtained by averaging over differences in fitted values based on the proportional hazards models. Large-sample properties of the proposed estimators are derived and simulation studies are conducted to assess their finite-sample applicability. We apply the proposed methods to liver wait list mortality data from the Scientific Registry of Transplant Recipients.
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