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Estimating Medical Costs with Censored Data
Heejung Bang and Anastasios A. Tsiatis
Vol. 87, No. 2 (Jun., 2000), pp. 329-343
Stable URL: http://www.jstor.org/stable/2673467
Page Count: 15
You can always find the topics here!Topics: Cost estimates, Estimators, Censorship, Health care costs, Estimated cost to complete, Statistical estimation, Estimators for the mean, Consistent estimators, Censored data, Simulations
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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.
Biometrika © 2000 Biometrika Trust