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Longitudinal analysis of censored medical cost data

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Author Info
Onur Başer (Thomson-Medstat, Ann Arbor, USA)
Joseph C. Gardiner (Division of Biostatistics, Department of Epidemiology, Michigan State University, USA)
Cathy J. Bradley (Department of Health Administration, Virginia Commonwealth University, Richmond, USA)
Hüseyin Yüce (Department of Mathematics, Florida International University, USA)
Charles Given (Department of Family Practice, Michigan State University, USA)
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.

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File URL: http://hdl.handle.net/10.1002/hec.1087
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Publisher Info
Article provided by John Wiley & Sons, Ltd. in its journal Health Economics.

Volume (Year): 15 (2006)
Issue (Month): 5 ()
Pages: 513-525
Download reference. The following formats are available: HTML (with abstract), plain text (with abstract), BibTeX, RIS (EndNote, RefMan, ProCite), ReDIF
Handle: RePEc:wly:hlthec:v:15:y:2006:i:5:p:513-525

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Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/5749

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  1. Blough, David K. & Madden, Carolyn W. & Hornbrook, Mark C., 1999. "Modeling risk using generalized linear models," Journal of Health Economics, Elsevier, vol. 18(2), pages 153-171, April. [Downloadable!] (restricted)
  2. White, Halbert, 1980. "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity," Econometrica, Econometric Society, vol. 48(4), pages 817-38, May. [Downloadable!] (restricted)
  3. Horowitz, Joel L. & Manski, Charles F., 1998. "Censoring of outcomes and regressors due to survey nonresponse: Identification and estimation using weights and imputations," Journal of Econometrics, Elsevier, vol. 84(1), pages 37-58, May. [Downloadable!] (restricted)
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  4. Hausman, Jerry A, 1978. "Specification Tests in Econometrics," Econometrica, Econometric Society, vol. 46(6), pages 1251-71, November. [Downloadable!] (restricted)
  5. Newey, Whitney K. & McFadden, Daniel, 1986. "Large sample estimation and hypothesis testing," Handbook of Econometrics, in: R. F. Engle & D. McFadden (ed.), Handbook of Econometrics, edition 1, volume 4, chapter 36, pages 2111-2245 Elsevier. [Downloadable!] (restricted)
  6. Manning, Willard G. & Basu, Anirban & Mullahy, John, 2005. "Generalized modeling approaches to risk adjustment of skewed outcomes data," Journal of Health Economics, Elsevier, vol. 24(3), pages 465-488, May. [Downloadable!] (restricted)
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  7. Manning, Willard G. & Mullahy, John, 2001. "Estimating log models: to transform or not to transform?," Journal of Health Economics, Elsevier, vol. 20(4), pages 461-494, July. [Downloadable!] (restricted)
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