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Longitudinal analysis of censored medical cost data

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  • Onur Başer
  • Joseph C. Gardiner
  • Cathy J. Bradley
  • Hüseyin Yüce
  • Charles Given

Abstract

This paper applies the inverse probability weighted (IPW) least‐squares method to estimate the effects of treatment on total medical cost, subject to censoring, in a panel‐data setting. IPW pooled ordinary‐least squares (POLS) and IPW random effects (RE) models are used. Because total medical cost might not be independent of survival time under administrative censoring, unweighted POLS and RE cannot be used with censored data, to assess the effects of certain explanatory variables. Even under the violation of this independency, IPW estimation gives consistent asymptotic normal coefficients with easily computable standard errors. A traditional and robust form of the Hausman test can be used to compare weighted and unweighted least squares estimators. The methods are applied to a sample of 201 Medicare beneficiaries diagnosed with lung cancer between 1994 and 1997. Copyright © 2006 John Wiley & Sons, Ltd.

Suggested Citation

  • Onur Başer & Joseph C. Gardiner & Cathy J. Bradley & Hüseyin Yüce & Charles Given, 2006. "Longitudinal analysis of censored medical cost data," Health Economics, John Wiley & Sons, Ltd., vol. 15(5), pages 513-525, May.
    • Handle: RePEc:wly:hlthec:v:15:y:2006:i:5:p:513-525
    DOI: 10.1002/hec.1087
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    References listed on IDEAS

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    Cited by:

    1. Y. T. Hwang & C. H. Huang & W. L. Yeh & Y. D. Shen, 2017. "The weighted general linear model for longitudinal medical cost data – an application in colorectal cancer," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(2), pages 288-307, January.
    2. Liu, Lei & Conaway, Mark R. & Knaus, William A. & Bergin, James D., 2008. "A random effects four-part model, with application to correlated medical costs," Computational Statistics & Data Analysis, Elsevier, vol. 52(9), pages 4458-4473, May.
    3. Maria Raikou & Alistair McGuire, 2012. "Estimating Costs for Economic Evaluation," Chapters, in: Andrew M. Jones (ed.), The Elgar Companion to Health Economics, Second Edition, chapter 43, Edward Elgar Publishing.
    4. Wouterse, B. & Meijboom, B.R. & Polder, J.J., 2011. "The relationship between baseline health and longitudinal costs of hospital use," Other publications TiSEM bdedc33c-9737-4bfc-beee-0, Tilburg University, School of Economics and Management.
    5. Pullenayegum Eleanor M & Willan Andrew R, 2011. "Marginal Models for Censored Longitudinal Cost Data: Appropriate Working Variance Matrices in Inverse-Probability-Weighted GEEs Can Improve Precision," The International Journal of Biostatistics, De Gruyter, vol. 7(1), pages 1-27, February.
    6. Lu Deng & Wendy Lou & Nicholas Mitsakakis, 2019. "Modeling right-censored medical cost data in regression and the effects of covariates," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 28(1), pages 143-155, March.
    7. Zhao, Xiaobing & Zhou, Xian, 2009. "Semiparametric modeling of medical cost data containing zeros," Statistics & Probability Letters, Elsevier, vol. 79(9), pages 1207-1214, May.
    8. Bram Wouterse & Bert R. Meijboom & Johan J. Polder, 2011. "The relationship between baseline health and longitudinal costs of hospital use," Health Economics, John Wiley & Sons, Ltd., vol. 20(8), pages 985-1008, August.

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