Estimating medical care costs under conditions of censoring

A number of non-parametric estimators have been proposed to calculate average medical care costs in the presence of censoring. This paper assesses their performance both in terms of bias and efficiency under extreme conditions using a medical dataset which exhibits heavy censoring. The estimators are further investigated using artificially generated data. Their variances are derived from analytic formulae based on the estimators' asymptotic properties and these are compared to empirically derived bootstrap estimates. The analysis revealed various performance patterns ranging from generally stable estimators under all conditions considered to estimators which become increasingly unstable with increasing levels of censoring. The bootstrap estimates of variance were consistent with the analytically derived asymptotic variance estimates. Of the two estimators that performed best, one imposes restrictions on the censoring distribution while the other is not restricted by the censoring pattern and on this basis the second may be preferred.