Estimating medical costs with censored data

Incompleteness of follow-up data is a common problem in estimating medical costs. Naive analysis using summary statistics on the collected data can re sult in severely misleading statistical inference. This paper focuses on th e problem of estimating the mean medical cost from a sample of individuals whose medical costs may be right censored. A class of weighted estimators w hich account appropriately for censoring are introduced. Our estimators are shown to be consistent and asymptotically normal with easily estimated var iances. The efficiency of these estimators is studied with the goal of find ing as efficient an estimator for the mean medical cost as is feasible. Ext ensive simulation studies are used to show that our estimators perform well in finite samples, even with heavily censored data, for a variety of circu mstances. The methods are applied to a set of cost data from a cardiology t rial conducted by the Duke University Medical Center. Extensions to other c ensored data problems are also discussed.