Estimation from Censored Medical Cost Data

This paper applies the inverse probability weighted least-squares method to predict total medical cost in the presence of censored data. Since survival time and medical costs may be subject to right censoring and therefore are not always observable, the ordinary least-squares approach cannot be used to assess the effects of explanatory variables. We demonstrate how inverse probability weighted least-squares estimation provides consistent asymptotic normal coefficients with easily computable standard errors. In addition, to assess the effect of censoring on coefficients, we develop a test comparing ordinary leastsquares and inverse probability weighted least-squares estimators. We demonstrate the methods developed by applying them to the estimation of cancer costs using Medicare claims data.