@article{Chen2015,
abstract = {In this paper, I show how to estimate the parameters of the beta-binomial distribution and its multivariate generalization, the Dirichlet-multinomial distribution. This approach involves no additional programming, as it relies on an existing Stata command used for overdispersed count panel data. Including covariates to allow for regression models based in these distributions is straightforward.},
archivePrefix = {arXiv},
arxivId = {The Stata Journal},
author = {Chen, Shuai and Rolfes, Jennifer and Zhao, Hongwei},
doi = {10.1177/1536867X1501500305},
eprint = {The Stata Journal},
isbn = {9796964600},
issn = {1536-867X},
journal = {The Stata Journal: Promoting communications on statistics and Stata},
keywords = {Censored data,Cost history,Cost-effectiveness analysis,Mean costs,hcost,st0399},
month = {oct},
number = {3},
pages = {698--711},
title = {{Estimation of Mean Health Care Costs and Incremental Cost-effectiveness Ratios with Possibly Censored Data}},
volume = {15},
year = {2015}
}

@article{Zhao2001,
abstract = {Medical cost estimation is very important to health care organizations and health policy makers. We consider cost-effectiveness analysis for competing treatments in a staggered-entry, survival-analysis-based clinical trial. We propose a method for estimating mean medical cost over patients in such settings. The proposed estimator is shown to be consistent and asymptotically normal, and its asymptotic variance can be obtained. In addition, we propose a method for estimating the incremental cost-effectiveness ratio and for obtaining a confidence interval for it. Simulation experiments are conducted to evaluate our proposed methods. Finally, we apply our methods to a clinical trial comparing the cost effectiveness of implanted cardiac defibrillators with conventional therapy for individuals at high risk for ventricular arrhythmias.},
author = {Zhao, Hongwei and Tian, Lili},
doi = {10.1111/j.0006-341X.2001.01002.x},
issn = {0006341X},
journal = {Biometrics},
keywords = {Counting process,Martingale process,Semiparametric efficiency,Survival analysis},
month = {dec},
number = {4},
pages = {1002--1008},
title = {{On Estimating Medical Cost and Incremental Cost-Effectiveness Ratios with Censored Data}},
volume = {57},
year = {2001}
}

@article{Bang2000,
abstract = {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 mislead- ing 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 esti- mators are shown to be consistent and asymptotically normal with easily estimated vari- ances. 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.},
author = {Bang, Heejung and Tsiatis, Anastasios A.},
doi = {10.1093/biomet/87.2.329},
isbn = {00063444},
issn = {00063444},
journal = {Biometrika},
keywords = {Cost analysis,Counting process,Efficiency,Martingale,Missing data,Semiparametrics,Survival analysis},
number = {2},
pages = {329--343},
title = {{Estimating medical costs with censored data}},
volume = {87},
year = {2000}
}

@article{Lin1997,
abstract = {Estimation of the average total cost for treating patients with a particular disease is often complicated by the fact that the survival times are censored on some study subjects and their subsequent costs are unknown. The naive sample average of the observed costs from all study subjects or from the uncensored cases only can be severely biased, and the standard survival analysis techniques are not applicable. To minimize the bias induced by censoring, we partition the entire time period of interest into a number of small intervals and estimate the average total cost either by the sum of the Kaplan-Meier estimator for the probability of dying in each interval multiplied by the sample mean of the total costs from the observed deaths in that interval or by the sum of the Kaplan-Meier estimator for the probability of being alive at the start of each interval multiplied by an appropriate estimator for the average cost over the interval conditional on surviving to the start of the interval. The resultant estimators are consistent if censoring occurs solely at the boundaries of the intervals. In addition, the estimators are asymptotically normal with easily estimated variances. Extensive numerical studies show that the asymptotic approximations are adequate for practical use and the biases of the proposed estimators are small even when censoring may occur in the interiors of the intervals. An ovarian cancer study is provided.},
author = {Lin, D. Y. and Feuer, E. J. and Etzioni, R. and Wax, Y.},
doi = {10.2307/2533947},
isbn = {0006-341X (Print)},
issn = {0006341X},
journal = {Biometrics},
number = {2},
pages = {419},
title = {{Estimating Medical Costs from Incomplete Follow-Up Data}},
volume = {53},
year = {1997}
}