@article{bang_tsiatis_2002, title={Median regression with censored cost data}, volume={58}, ISSN={["0006-341X"]}, DOI={10.1111/j.0006-341X.2002.00643.x}, abstractNote={Summary. Because of the skewness of the distribution of medical costs, we consider modeling the median as well as other quantiles when establishing regression relationships to covariates. In many applications, the medical cost data are also right censored. In this article, we propose semiparametric procedures for estimating the parameters in median regression models based on weighted estimating equations when censoring is present. Numerical studies are conducted to show that our estimators perform well with small samples and the resulting inference is reliable in circumstances of practical importance. The methods are applied to a dataset for medical costs of patients with colorectal cancer.}, number={3}, journal={BIOMETRICS}, author={Bang, H and Tsiatis, AA}, year={2002}, month={Sep}, pages={643–649} }
 @article{bang_tsiatis_2000, title={Estimating medical costs with censored data}, volume={87}, ISSN={["0006-3444"]}, DOI={10.1093/biomet/87.2.329}, abstractNote={Incompleteness of follow-up data is a common problem in estimating medical costs. Naive analysis using summary statistics on the collected data can result in severely misleading statistical inference. This paper focuses on the problem of estimating the mean medical cost from a sample of individuals whose medical costs may be right censored. A class of weighted estimators which account appropriately for censoring are introduced. Our estimators are shown to be consistent and asymptotically normal with easily estimated variances. The efficiency of these estimators is studied with the goal of finding as efficient an estimator for the mean medical cost as is feasible. Extensive simulation studies are used to show that our estimators perform well in finite samples, even with heavily censored data, for a variety of circumstances. The methods are applied to a set of cost data from a cardiology trial conducted by the Duke University Medical Center. Extensions to other censored data problems are also discussed.}, number={2}, journal={BIOMETRIKA}, author={Bang, H and Tsiatis, AA}, year={2000}, month={Jun}, pages={329–343} }