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Cost function estimation: the choice of a model to apply to dementia


  • Christian Kronborg Andersen
  • Kjeld Andersen
  • Per Kragh‐Sørensen


Statistical analysis of cost data is often difficult because of highly skewed data resulting from a few patients who incur high costs relative to the majority of patients. When the objective is to predict the cost for an individual patient, the literature suggests that one should choose a regression model based on the quality of its predictions. In exploring the econometric issues, the objective of this study was to estimate a cost function in order to estimate the annual health care cost of dementia. Using different models, health care costs were regressed on the degree of dementia, sex, age, marital status and presence of any co‐morbidity other than dementia. Models with a log‐transformed dependent variable, where predicted health care costs were re‐transformed to the unlogged original scale by multiplying the exponential of the expected response on the log‐scale with the average of the exponentiated residuals, were part of the considered models. The root mean square error (RMSE), the mean absolute error (MAE) and the Theil U‐statistic criteria were used to assess which model best predicted the health care cost. Large values on each criterion indicate that the model performs poorly. Based on these criteria, a two‐part model was chosen. In this model, the probability of incurring any costs was estimated using a logistic regression, while the level of the costs was estimated in the second part of the model. The choice of model had a substantial impact on the predicted health care costs, e.g. for a mildly demented patient, the estimated annual health care costs varied from DKK 71 273 to DKK 90 940 (US$ 1=DKK 7) depending on which model was chosen. For the two‐part model, the estimated health care costs ranged from DKK 44 714, for a very mildly demented patient, to DKK 197 840, for a severely demented patient. Copyright © 2000 John Wiley & Sons, Ltd.

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  • Christian Kronborg Andersen & Kjeld Andersen & Per Kragh‐Sørensen, 2000. "Cost function estimation: the choice of a model to apply to dementia," Health Economics, John Wiley & Sons, Ltd., vol. 9(5), pages 397-409, July.
  • Handle: RePEc:wly:hlthec:v:9:y:2000:i:5:p:397-409
    DOI: 10.1002/1099-1050(200007)9:5<397::AID-HEC527>3.0.CO;2-E

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    References listed on IDEAS

    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.
    2. Manning, W. G. & Duan, N. & Rogers, W. H., 1987. "Monte Carlo evidence on the choice between sample selection and two-part models," Journal of Econometrics, Elsevier, vol. 35(1), pages 59-82, May.
    3. Paul Mcnamee & Barbara A. Gregson & Ken Wright & Debbie Buck & Claire H. Bamford & John Bond, 1998. "Estimation of a multiproduct cost function for physically frail older people," Health Economics, John Wiley & Sons, Ltd., vol. 7(8), pages 701-710.
    4. Andrew H. Briggs & David E. Wonderling & Christopher Z. Mooney, 1997. "Pulling cost‐effectiveness analysis up by its bootstraps: A non‐parametric approach to confidence interval estimation," Health Economics, John Wiley & Sons, Ltd., vol. 6(4), pages 327-340, July.
    5. Leung, Siu Fai & Yu, Shihti, 1996. "On the choice between sample selection and two-part models," Journal of Econometrics, Elsevier, vol. 72(1-2), pages 197-229.
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    Cited by:

    1. Negri­n, Miguel A. & Vázquez-Polo, Francisco-José, 2008. "Incorporating model uncertainty in cost-effectiveness analysis: A Bayesian model averaging approach," Journal of Health Economics, Elsevier, vol. 27(5), pages 1250-1259, September.
    2. Ebere Akobundu & Jing Ju & Lisa Blatt & C. Mullins, 2006. "Cost-of-Illness Studies," PharmacoEconomics, Springer, vol. 24(9), pages 869-890, September.
    3. Michele Cecchini & Franco Sassi, 2015. "Preventing Obesity in the USA: Impact on Health Service Utilization and Costs," PharmacoEconomics, Springer, vol. 33(7), pages 765-776, July.
    4. F. J. Vázquez‐Polo & M. A. Negrín Hernández & B. González López‐Valcárcel, 2005. "Using covariates to reduce uncertainty in the economic evaluation of clinical trial data," Health Economics, John Wiley & Sons, Ltd., vol. 14(6), pages 545-557, June.
    5. Björn Stollenwerk & Thomas Welchowski & Matthias Vogl & Stephanie Stock, 2016. "Cost-of-illness studies based on massive data: a prevalence-based, top-down regression approach," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 17(3), pages 235-244, April.
    6. Francisco-José Polo & Miguel Negrín & Xavier Badía & Montse Roset, 2005. "Bayesian regression models for cost-effectiveness analysis," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 6(1), pages 45-52, March.
    7. Ahmadi, Sadra & Yeh, Chung-Hsing & Martin, Rodney & Papageorgiou, Elpiniki, 2015. "Optimizing ERP readiness improvements under budgetary constraints," International Journal of Production Economics, Elsevier, vol. 161(C), pages 105-115.

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