Abstract #300494

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JSM 2003 Abstract #300494
Activity Number: 372
Type: Contributed
Date/Time: Wednesday, August 6, 2003 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract - #300494
Title: Regression Analysis of Medical Costs with Right-Censored Data in the Presence of Discrete Covariates
Author(s): Mohammad H. Rahbar*+ and Alla Sikorskii and Joseph C. Gardiner
Companies: Michigan State University and Michigan State University and Michigan State University
Address: 100 Conrad Hall, East Lansing, MI, 48824-1327,
Keywords: medical costs ; right-censored data ; regression analysis ; discrete covariates ; survival analysis ; nonparametric method
Abstract:

We study regression analysis of medical cost data with incomplete cost assessment. Several authors have cautioned that inherent patient heterogeneity with respect to cost accumulation leads to positive correlation between the cumulative cost at the censoring time and at the end point of interest which makes traditional survival analysis methods invalid. We propose an alternative method for analysis of censored cost data, based on Rahbar & Gardiner (1995). We compare the efficiency of our method with the method proposed by Lin (2000). Specifically, the follow up period of interest [0, L] is divided into a fixed number of intervals of equal length. For the simulation, within each interval, we consider a baseline cost of uniform (0,1), a diagnostic cost of uniform (0,1) at t = 0, and a uniform (0,1) cost in the final year of life. The covariate of interest is a treatment indicator. Our results indicate that with discrete covariates our method might produce more efficient estimators as compared to the procedure proposed by Lin. Finally, the advantages and disadvantages of our procedures are discussed, particularly the impact of the degree of censoring on the estimated standard errors.

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