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A simple but efficient approach to the analysis of multilevel data

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Abstract

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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  • Bache, Stefan Holst Milton & Kristensen, Troels, 2013. "A simple but efficient approach to the analysis of multilevel data," COHERE Working Paper 2013:6, University of Southern Denmark, COHERE - Centre of Health Economics Research.
  • Handle: RePEc:hhs:sduhec:2013_006
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    References listed on IDEAS

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    1. Mundlak, Yair, 1978. "On the Pooling of Time Series and Cross Section Data," Econometrica, Econometric Society, vol. 46(1), pages 69-85, January.
    2. Hausman, Jerry, 2015. "Specification tests in econometrics," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 38(2), pages 112-134.
    3. 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.
    4. 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.
    5. Lewis, Jeffrey B. & Linzer, Drew A., 2005. "Estimating Regression Models in Which the Dependent Variable Is Based on Estimates," Political Analysis, Cambridge University Press, vol. 13(04), pages 345-364, September.
    6. Richard Blundell & Frank Windmeijer, 1997. "Cluster effects and simultaneity in multilevel models," Health Economics, John Wiley & Sons, Ltd., vol. 6(4), pages 439-443.
    7. 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.
    8. Nigel Rice & Andrew Jones, 1997. "Multilevel models and health economics," Health Economics, John Wiley & Sons, Ltd., vol. 6(6), pages 561-575.
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    More about this item

    Keywords

    Multilevel models; random intercepts; nested models; Mundlak device; correlated random effects; 2-step estimation; estimated dependent variables; fee-for-service expenditures; type 2 diabetes;

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • H51 - Public Economics - - National Government Expenditures and Related Policies - - - Government Expenditures and Health
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health

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