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A simple but efficient approach to the analysis of multilevel data

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    Much research in health economics revolves around the analysis of hierarchically structured data. For instance, combining characteristics of patients with information pertaining to the general practice (GP) clinic providing treatment is called for in order to investigate important features of the underlying nested structure. In this paper we offer a new treatment of the two-level random-intercept model and state equivalence results for specific estimators, including popular two-step estimators. We show that a certain encompassing regression equation, based on a Mundlak-type specification, provides a surprisingly simple approach to efficient estimation and a straightforward way to assess the assumptions required. As an illustration, we combine unique information on the morbidity of Danish type 2 diabetes patients with information about GP clinics to investigate the association with fee-for-service healthcare expenditure. Our approach allows us to conclude that explanatory power is mainly provided by patient information and patient mix, whereas (possibly unobserved) clinic characteristics seem to play a minor role.

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    File URL: http://static.sdu.dk/mediafiles//0/5/2/%7B052F4DC9-E33F-4BAC-AA77-6CCC13BD5398%7D2013_6.pdf
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    Paper provided by COHERE - Centre of Health Economics Research, University of Southern Denmark in its series COHERE Working Paper with number 2013:6.

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    Length: 30 pages
    Date of creation: 20 Jun 2013
    Date of revision:
    Handle: RePEc:hhs:sduhec:2013_006
    Contact details of provider: Postal: COHERE - Center for Sundhedsøkonomisk Forskning, Institut for Virksomhedsledelse og Økonomi, Syddansk Universitet, Campusvej 55, DK-5230 Odense M, Denmark
    Phone: (+45) 6550 3081
    Fax: (+45) 6550 3880
    Web page: http://www.sdu.dk/Om_SDU/Institutter_centre/cCohere
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    1. Laudicella, Mauro & Olsen, Kim Rose & Street, Andrew, 2010. "Examining cost variation across hospital departments-a two-stage multi-level approach using patient-level data," Social Science & Medicine, Elsevier, vol. 71(10), pages 1872-1881, November.
    2. Nigel Rice & Andrew Jones, 1997. "Multilevel models and health economics," Health Economics, John Wiley & Sons, Ltd., vol. 6(6), pages 561-575.
    3. Hausman, Jerry, 2015. "Specification tests in econometrics," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 38(2), pages 112-134.
    4. Richard Blundell & Frank Windmeijer, 1997. "Cluster effects and simultaneity in multilevel models," Health Economics, John Wiley & Sons, Ltd., vol. 6(4), pages 439-443.
    5. Kathleen Carey, 2000. "A multilevel modelling approach to analysis of patient costs under managed care," Health Economics, John Wiley & Sons, Ltd., vol. 9(5), pages 435-446.
    6. Mundlak, Yair, 1978. "On the Pooling of Time Series and Cross Section Data," Econometrica, Econometric Society, vol. 46(1), pages 69-85, January.
    7. Jaya Krishnakumar, 2003. "Time Invariant Variables and Panel Data Models : A Generalised Frisch-Vaugh Theorem and its Implications," Research Papers by the Institute of Economics and Econometrics, Geneva School of Economics and Management, University of Geneva 2004.01, Institut d'Economie et Econométrie, Université de Genève.
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