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Cost prediction models for the comparison of two groups

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  • Andrew R. Willan
  • Bernie J. O'Brien

Abstract

For trial‐based economic evaluation where patient‐specific cost data are not routinely available, cost prediction models are commonly used to estimate total cost for each patient. Typically, multiple regression techniques are used on data from diagnosis‐matched, non‐trial patients (where patient‐level cost data are available) to model cost as a function of covariates that are observed on the trial subjects (e.g. length of hospital stay, procedures, etc.). The estimated beta coefficients provide a means of estimating the total cost for each patient in the trial. However, the variability of the beta coefficients due the measurement and sampling error is seldom included in the overall variance expression for mean costs by treatment group. In this paper we provide a method for estimating this variance and provide an example application Copyright © 2001 John Wiley & Sons, Ltd.

Suggested Citation

  • Andrew R. Willan & Bernie J. O'Brien, 2001. "Cost prediction models for the comparison of two groups," Health Economics, John Wiley & Sons, Ltd., vol. 10(4), pages 363-366, June.
    • Handle: RePEc:wly:hlthec:v:10:y:2001:i:4:p:363-366
    DOI: 10.1002/hec.615
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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. 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.
    3. Kelvin K. W. Chan & Andrew R. Willan & Michael Gupta & Eleanor Pullenayegum, 2014. "Underestimation of Uncertainties in Health Utilities Derived from Mapping Algorithms Involving Health-Related Quality-of-Life Measures," Medical Decision Making, , vol. 34(7), pages 863-872, October.
    4. Grégoire Mercier & Gérald Naro, 2014. "Costing Hospital Surgery Services: The Method Matters," Post-Print hal-01829947, HAL.
    5. 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.
    6. Miguel A. Negrín & Francisco J. Vázquez-Polo & María Martel & Elías Moreno & Francisco J. Girón, 2010. "Bayesian Variable Selection in Cost-Effectiveness Analysis," IJERPH, MDPI, vol. 7(4), pages 1-20, April.

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