Estimation from censored medical cost data

• Main Author: Baser, Onur
• Format: Dissertation
• Language: English
• Published: ProQuest Dissertations & Theses 01.01.2002
• Subjects: Cancer therapies; Comorbidity; Economic theory; Economics; Health care expenditures; Probability; Software; Studies
• ISBN: 0493686347; 9780493686349

Health care inflation is a concern in many industrialized countries. One response is search for cost effective therapies which requires proper analysis of treatment cost data. Common problem with medical cost data is censoring and statistical properties of estimating medical cost from a censored data is not well developed. In my thesis, I propose two method, one with an extension to panel data setting, to estimate medical cost from censored data. First chapter applies the inverse probability weighted least-squares method to predict censored total medical cost. 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 certain explanatory variables. Inverse probability weighted least-squares estimation provides consistent asymptotic normal coefficients with easily computable standard errors. A test is derived to compare the differences between the coefficients estimated by the ordinary least-squares approach and the inverse probability weighted least-squares estimation. A study on the medical cost of lung cancer is used as an application of the method. Second chapter applies the inverse probability weighted (IPW) least-squares method to predict total medical cost from panel data subject to censoring. Specifically, IPW pooled ordinary-least squares (POLS) and IPW random effects (RE) models are used. Because total medical cost is not independent of the survival time under administrative censoring, unweighted POLS and RE cannot be used with uncensored data, to assess the effects of certain explanatory variables. IPW estimation gives consistent asymptotic normal coefficients with easily computable standard errors. A traditional and robust form of Hausman test can be used to compare the coefficients estimated by weighted and unweighted estimation methods. The method developed in this paper are applied to lung cancer cost data. In the third chapter, a method for testing and correcting for sample selection bias for cross-sectional data is proposed. Specifically, this paper provides a systematic treatment of the correction for nonrandom sample selection of medical cost data where the selection rule is described by a censored regression model. We show that the population parameters are identified, and provide straightforward [special characters omitted]-consistent and asymptotically normal estimation methods under the assumption that the selection rule is governed by a censored Tobit Model. A study on the medical cost of lung cancer is used as an application of the method.