title: Propensity score and doubly robust methods for estimating the effect of treatment on censored cost.
abstract: The estimation of treatment effects on medical costs is complicated by the need to account for informative censoring, skewness, and the effects of confounders. Because medical costs are often collected from observational claims data, we investigate propensity score (PS) methods such as covariate adjustment, stratification, and inverse probability weighting taking into account informative censoring of the cost outcome. We compare these more commonly used methods with doubly robust (DR) estimation. We then use a machine learning approach called super learner (SL) to choose among conventional cost models to estimate regression parameters in the DR approach and to choose among various model specifications for PS estimation. Our simulation studies show that when the PS model is correctly specified, weighting and DR perform well. When the PS model is misspecified, the combined approach of DR with SL can still provide unbiased estimates. SL is especially useful when the underlying cost distribution comes from a mixture of different distributions or when the true PS model is unknown. We apply these approaches to a cost analysis of two bladder cancer treatments, cystectomy versus bladder preservation therapy, using SEER-Medicare data. Copyright © 2015 John Wiley & Sons, Ltd.
authors.affiliation: Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, PA, 19104, U.S.A.
authors.forename: Jiaqi
authors.initials: J
authors.lastname: Li
authors.affiliation: Biostatistics and Bioinformatics Facility, Temple University Health System Fox Chase Cancer Center, Philadelphia, PA, 19111, U.S.A.
authors.forename: Elizabeth
authors.initials: E
authors.lastname: Handorf
authors.affiliation: Department of Radiation Oncology, University of Pennsylvania, Philadelphia, PA, 19104, U.S.A.
authors.forename: Justin
authors.initials: J
authors.lastname: Bekelman
authors.affiliation: Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, PA, 19104, U.S.A.
authors.forename: Nandita
authors.initials: N
authors.lastname: Mitra
mesh_terms: D003362:Cost-Benefit Analysis / Q000706:statistics & numerical data*; D003627:Data Interpretation, Statistical; D017048:Health Care Costs / Q000706:statistics & numerical data*; D006801:Humans; D000069550:Machine Learning; D015233:Models, Statistical; D011336:Probability; D057216:Propensity Score; D016896:Treatment Outcome
keywords: cost estimation; doubly robust estimation; observational studies; propensity scores; super learner