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A random effects four-part model, with application to correlated medical costs

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  • Liu, Lei
  • Conaway, Mark R.
  • Knaus, William A.
  • Bergin, James D.

Abstract

In this paper we propose a four-part random effects model, with application to correlated medical cost data. Four joint equations are used to model respectively: (1) the probability of seeking medical treatment, (2) the probability of being hospitalized (conditional on seeking medical treatment), and the actual amount of (3) outpatient and (4) inpatient costs. Our model simultaneously takes account of the inter-temporal (or within-cluster) correlation of each patient and the cross-equation correlation of the four equations, by means of joint linear mixed models and generalized linear mixed models. The estimation is accomplished by the high-order Laplace approximation technique in Raudenbush et al. [Raudenbush, S.W., Yang, M., Yosef, M., 2000. Maximum likelihood for generalized linear models with nested random effects via high-order, multivariate Laplace approximation. Journal of Computational and Graphical Statistics 9, 141-157] and Olsen and Schafer [Olsen, M.K., Schafer, J.L., 2001. A two-part random effects model for semicontinuous longitudinal data. Journal of the American Statistical Association 96, 730-745]. Our model is used to analyze monthly medical costs of 1397 chronic heart failure patients from the clinical data repository (CDR) at the University of Virginia.

Suggested Citation

  • Liu, Lei & Conaway, Mark R. & Knaus, William A. & Bergin, James D., 2008. "A random effects four-part model, with application to correlated medical costs," Computational Statistics & Data Analysis, Elsevier, vol. 52(9), pages 4458-4473, May.
    • Handle: RePEc:eee:csdana:v:52:y:2008:i:9:p:4458-4473
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    2. Qi Liu & Jin Liu & Shuntian Sui, 2020. "Public Medical Insurance and Healthcare Utilization and Expenditures of Older with Chronic Diseases in Rural China: Evidence from NRCMS," IJERPH, MDPI, vol. 17(20), pages 1-15, October.
    3. Arnab Mukherji & Satrajit Roychowdhury & Pulak Ghosh & Sarah Brown, 2012. "Estimating Healthcare Demand for an Aging Population: A Flexible and Robust Bayesian Joint Model," Working Papers 2012027, The University of Sheffield, Department of Economics.
    4. Lu Deng & Wendy Lou & Nicholas Mitsakakis, 2019. "Modeling right-censored medical cost data in regression and the effects of covariates," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 28(1), pages 143-155, March.

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