ccostrRef.bib

@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 = {0006-3444},
journal = {Biometrika},
keywords = {Cost analysis,Counting process,Efficiency,Martingale,Missing data,Semiparametrics,Survival analysis},
month = {jun},
number = {2},
pages = {329--343},
pmid = {304515374},
title = {{Estimating medical costs with censored data}},
volume = {87},
year = {2000}
}

@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 = {1536867X},
journal = {Stata Journal},
keywords = {Censored data,Cost history,Cost-effectiveness analysis,Mean costs,hcost,st0399},
number = {3},
pages = {698--711},
pmid = {25080530},
title = {{Estimation of mean health care costs and incremental cost-effectiveness ratios with possibly censored data}},
volume = {15},
year = {2015}
}

@article{Kim2011,
abstract = {SUMMARY Cost-effectiveness analysis is usually based on life-years gained estimated from all-cause mortality. When an intervention affects only a few causes of death accounting for a small fraction of all deaths, this approach may lack precision. We develop a novel technique for cost-effectiveness analysis when life-years gained are estimated from cause-specific mortality, allowing for competing causes of death. In the context of randomised trial data, we adjust for other-cause mortality combined across randomised groups. This method yields a greater precision than analysis based on total mortality, and we show application to life-years gained, quality-adjusted life-years gained, incremental costs, and cost effectiveness. In multi-state health economic models, however, mortality from competing causes is commonly derived from national statistics and is assumed to be known and equal across intervention groups. In such models, our method based on cause-specific mortality and standard methods using total mortality give essentially identical estimates and precision. The methods are applied to a randomised trial and a health economic model, both of screening for abdominal aortic aneurysm. A gain in precision for cost-effectiveness estimates is clearly helpful for decision making, but it is important to ensure that 'cause-specific mortality' is defined to include all causes of death potentially affected by the intervention.},
annote = {Artikkel omkring den f{\o}rste Stata pakke},
archivePrefix = {arXiv},
arxivId = {10.1002/hec.3108},
author = {Kim, Lois G. and Thompson, Simon G.},
doi = {10.1002/hec.1648},
eprint = {hec.3108},
isbn = {1099-1050; 1057-9230},
issn = {10579230},
journal = {Health Economics},
keywords = {Cause-specific mortality,Competing risks,Cost-effectiveness analysis,Life-years gained},
month = {jul},
number = {7},
pages = {842--852},
pmid = {20799342},
primaryClass = {10.1002},
title = {{Estimation of life-years gained and cost effectiveness based on cause-specific mortality}},
volume = {20},
year = {2011}
}

@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},
month = {jun},
number = {2},
pages = {419},
pmid = {9192444},
title = {{Estimating Medical Costs from Incomplete Follow-Up Data}},
volume = {53},
year = {1997}
}

@article{Pfeifer2005,
abstract = {This paper is about how to use data from a random sample of customer relationships to calculate an appropriate average customer lifetime value (CLV). When the sample contains only completed relationships, the simple unweighted average is appropriate. When the sample contains a mix of active and completed relationships, the lifetimes of the active relationships are said to be right censored because the observed lifetime to date is but a lower bound on the eventual lifetime. Because of this censoring, a simple average of the sample CLVs to date will be a biased estimate of the mean CLV. This paper presents and explores several non-parametric estimation methods for correcting for this bias.},
author = {Pfeifer, Phillip E. and Bang, Heejung},
doi = {10.1002/dir.20049},
issn = {10949968},
journal = {Journal of Interactive Marketing},
month = {jan},
number = {4},
pages = {48--66},
title = {{Non-parametric estimation of mean customer lifetime value}},
volume = {19},
year = {2005}
}


@article{Zhao2007,
abstract = {In clinical trials comparing different treatments and in health economics and outcomes research, medical costs are frequently analysed to evaluate the economical impacts of new treatment options and economic values of health-care utilization. Since Lin et al.'s first finding in the problem of applying the survival analysis techniques to the cost data, many new methods have been proposed. In this report, we establish analytic relationships among several widely adopted medical cost estimators that are seemingly different. Specifically, we report the equivalence among various estimators that were introduced by Lin et al., Bang and Tsiatis, and Zhao and Tian. Lin's estimators are formerly known to be asymptotically unbiased in some discrete censoring situations and biased otherwise, whereas all other estimators discussed here are consistent for the expected medical cost. Thus, we identify conditions under which these estimators become identical and, consequently, the biased estimators achieve consistency. We illustrate these relationships using an example from a clinical trial examining the effectiveness of implantable cardiac defibrillators in preventing death among people who had prior myocardial infarctions.},
archivePrefix = {arXiv},
arxivId = {NIHMS150003},
author = {Zhao, Hongwei and Bang, Heejung and Wang, Hongkun and Pfeifer, Phillip E.},
doi = {10.1002/sim.2882},
eprint = {NIHMS150003},
isbn = {2007090091480},
issn = {02776715},
journal = {Statistics in Medicine},
keywords = {Censoring,Cost analysis,Inverse probability-weighting,Survival analysis},
month = {oct},
number = {24},
pages = {4520--4530},
pmid = {19455509},
title = {{On the equivalence of some medical cost estimators with censored data}},
volume = {26},
year = {2007}
}



@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 = {0006-341X},
journal = {Biometrics},
keywords = {Counting process,Martingale process,Semiparametric efficiency,Survival analysis},
month = {dec},
number = {4},
pages = {1002--1008},
pmid = {11764238},
title = {{On estimating medical cost and incremental cost-effectiveness ratios with censored data.}},
volume = {57},
year = {2001}
}

@incollection{Zhao2010,
author = {Zhao, Honwei and Wang, Hongkun},
booktitle = {Analysis of Observational Health Care Data Using {SAS}},
editor = {Faries, Douglas E. and Obenchain, R. and Haro, Josep M. and Leon, Andrew C.},
pages = {363--381},
publisher = {SAS Press},
title = {{Cost and Cost-Effectiveness Analysis with Censored Data}},
year = {2010}
}