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Longitudinal analysis of censored medical cost data Author info | Abstract | Publisher info | Download info | Related research | Statistics 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)
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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Article provided by John Wiley & Sons, Ltd. in its journal Health Economics .
Volume (Year): 15 (2006)
Issue (Month): 5 ()
Pages: 513-525
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Handle: RePEc:wly:hlthec:v:15:y:2006:i:5:p:513-525Contact details of provider: Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/5749
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