Many analyses of healthcare costs involve use of data with varying periods of observation and right censoring of cases before death or at the end of the episode of illness. The prominence of observations with no expenditure for some short periods of observation and the extreme skewness typical of these data raise concerns about the robustness of estimators based on inverse probability weighting (IPW) with the survival from censoring probabilities. These estimators also cannot distinguish between the effects of covariates on survival and intensity of utilization, which jointly determine costs. In this paper, we propose a new estimator that extends the class of two-part models to deal with random right censoring and for continuous death and censoring times. Our model also addresses issues about the time to death in these analyses and separates the survival effects from the intensity effects. Using simulations, we compare our proposed estimator to the inverse probability estimator, which shows bias when censoring is large and covariates affect survival. We find our estimator to be unbiased and also more efficient for these designs. We apply our method and compare it with the IPW method using data from the Medicare-SEER files on prostate cancer. Copyright © 2010 John Wiley & Sons, Ltd.
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