Institute of Mathematical Statistics Collections
Estimating medical costs from a transition model
Nonparametric estimators of the mean total cost have been proposed in a variety of settings. In clinical trials it is generally impractical to follow up patients until all have responded, and therefore censoring of patient outcomes and total cost will occur in practice. We describe a general longitudinal framework in which costs emanate from two streams, during sojourn in health states and in transition from one health state to another. We consider estimation of net present value for expenditures incurred over a finite time horizon from medical cost data that might be incompletely ascertained in some patients. Because patient specific demographic and clinical characteristics would influence total cost, we use a regression model to incorporate covariates. We discuss similarities and differences between our net present value estimator and other widely used estimators of total medical costs. Our model can accommodate heteroscedasticity, skewness and censoring in cost data and provides a flexible approach to analyses of health care cost.
First available in Project Euclid: 1 April 2008
Permanent link to this document
Digital Object Identifier
Mathematical Reviews number (MathSciNet)
Primary: 62N01: Censored data models 60J27: Continuous-time Markov processes on discrete state spaces
Secondary: 62G05: Estimation
Copyright © 2008, Institute of Mathematical Statistics
Gardiner, Joseph C.; Liu, Lin; Luo, Zhehui. Estimating medical costs from a transition model. Beyond Parametrics in Interdisciplinary Research: Festschrift in Honor of Professor Pranab K. Sen, 350--363, Institute of Mathematical Statistics, Beachwood, Ohio, USA, 2008. doi:10.1214/193940307000000266. https://projecteuclid.org/euclid.imsc/1207058285
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