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Comparing alternative models: log vs Cox proportional hazard?

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Author Info
Anirban Basu (Harris School of Public Policy, The University of Chicago, Chicago, USA)
Willard G. Manning
John Mullahy

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Abstract

Health economists often use log models (based on OLS or generalized linear models) to deal with skewed outcomes such as those found in health expenditures and inpatient length of stay. Some recent studies have employed Cox proportional hazard regression as a less parametric alternative to OLS and GLM models, even when there was no need to correct for censoring. This study examines how well the alternative estimators behave econometrically in terms of bias when the data are skewed to the right. Specifically we provide evidence on the performance of the Cox model under a variety of data generating mechanisms and compare it to the estimators studied recently in Manning and Mullahy (2001). No single alternative is best under all of the conditions examined here. However, the gamma regression model with a log link seems to be more robust to alternative data generating mechanisms than either OLS on ln(y) or Cox proportional hazards regression. We find that the proportional hazard assumption is an essential requirement to obtain consistent estimate of the E(y∣x) using the Cox model. Copyright © 2004 John Wiley & Sons, Ltd.

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File URL: http://hdl.handle.net/10.1002/hec.852
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Publisher Info
Article provided by John Wiley & Sons, Ltd. in its journal Health Economics.

Volume (Year): 13 (2004)
Issue (Month): 8 ()
Pages: 749-765
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Handle: RePEc:wly:hlthec:v:13:y:2004:i:8:p:749-765

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

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  1. Mullahy, John, 1998. "Much ado about two: reconsidering retransformation and the two-part model in health econometrics," Journal of Health Economics, Elsevier, vol. 17(3), pages 247-281, June. [Downloadable!] (restricted)
  2. 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)
  3. Etzioni, Ruth D. & Feuer, Eric J. & Sullivan, Sean D. & Lin, Danyu & Hu, Chengcheng & Ramsey, Scott D., 1999. "On the use of survival analysis techniques to estimate medical care costs," Journal of Health Economics, Elsevier, vol. 18(3), pages 365-380, June. [Downloadable!] (restricted)
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  1. Anirban Basu & James J. Heckman & Salvador Navarro-Lozano & Sergio Urzua, 2007. "Use of instrumental variables in the presence of heterogeneity and self-selection: An application in breast cancer patients," Health, Econometrics and Data Group (HEDG) Working Papers 07/07, HEDG, c/o Department of Economics, University of York. [Downloadable!]
  2. Manos Matsaganis & Theodore Mitrakos & Panos Tsakloglou, 2009. "Modelling health expenditure at the household level in Greece," The European Journal of Health Economics, Springer, vol. 10(3), pages 329-336, July. [Downloadable!] (restricted)
  3. Anirban Basu & James J. Heckman & Salvador Navarro-Lozano & Sergio Urzua, 2007. "Use of instrumental variables in the presence of heterogeneity and self-selection: an application to treatments of breast cancer patients," Health Economics, John Wiley & Sons, Ltd., vol. 16(11), pages 1133-1157. [Downloadable!]
  4. Willard G. Manning & Anirban Basu & John Mullahy, 2003. "Generalized Modeling Approaches to Risk Adjustment of Skewed Outcomes Data," Working Papers 0313, Harris School of Public Policy Studies, University of Chicago. [Downloadable!]
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  5. Manos Matsaganis & Theodore Mitrakos & Panos Tsakloglou, 2008. "Modelling Household Expenditure on Health Care in Greece," Working Papers 68, Bank of Greece. [Downloadable!]
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