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Estimating the cost‐effectiveness of an intervention in a clinical trial when partial cost information is available: a Bayesian approach

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  • Paul C. Lambert
  • Lucinda J. Billingham
  • Nicola J. Cooper
  • Alex J. Sutton
  • Keith R. Abrams

Abstract

There is an increasing need to establish whether health‐care interventions are cost effective as well as clinically effective. It is becoming increasingly common for cost studies to be incorporated into clinical trials, either on all patients or more usually on a subset of patients. Establishing the total cost per patient is complex, as it requires information on resource use, which may come from a variety of different sources. This complexity may lead to considerable missing data, and can result in some patients only having partial cost information. In this paper we consider a clinical trial consisting of 351 patients with advanced non‐small cell lung cancer comparing chemotherapy with standard palliative care. A subset of 115 patients was selected for the cost sub‐study. Total cost was split into four components, for which resource use was collected. Complete resource data were available on 82 patients. For the remaining patients at least one of the cost components was missing. The objective of this paper is to develop a Bayesian approach which simultaneously models both the clinical effectiveness data and the cost data, by modelling the individual components. This also provides estimates of the cost‐effectiveness in terms of the Incremental Net Monetary Benefit (INMB) and Cost‐Effectiveness Acceptability Curves (CEAC). We compare a number of different models of increasing complexity. The models estimate the interrelationships between the four cost components and survival, and thus enable a predictive distribution for each missing cost item to be obtained. Copyright © 2007 John Wiley & Sons, Ltd.

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  • Paul C. Lambert & Lucinda J. Billingham & Nicola J. Cooper & Alex J. Sutton & Keith R. Abrams, 2008. "Estimating the cost‐effectiveness of an intervention in a clinical trial when partial cost information is available: a Bayesian approach," Health Economics, John Wiley & Sons, Ltd., vol. 17(1), pages 67-81, January.
    • Handle: RePEc:wly:hlthec:v:17:y:2008:i:1:p:67-81
    DOI: 10.1002/hec.1243
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    References listed on IDEAS

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    1. Manuel Gomes & Karla Díaz-Ordaz & Richard Grieve & Michael G. Kenward, 2013. "Multiple Imputation Methods for Handling Missing Data in Cost-effectiveness Analyses That Use Data from Hierarchical Studies," Medical Decision Making, , vol. 33(8), pages 1051-1063, November.
    2. Daniel P Beavers & James D Stamey, 2018. "Bayesian sample size determination for cost-effectiveness studies with censored data," PLOS ONE, Public Library of Science, vol. 13(1), pages 1-16, January.
    3. Aline Gauthier & Andrea Manca & Susan Anton, 2009. "Bayesian Modelling of Healthcare Resource Use in Multinational Randomized Clinical Trials," PharmacoEconomics, Springer, vol. 27(12), pages 1017-1029, December.
    4. Alexina J. Mason & Manuel Gomes & Richard Grieve & James R. Carpenter, 2018. "A Bayesian framework for health economic evaluation in studies with missing data," Health Economics, John Wiley & Sons, Ltd., vol. 27(11), pages 1670-1683, November.
    5. Rita Faria & Manuel Gomes & David Epstein & Ian White, 2014. "A Guide to Handling Missing Data in Cost-Effectiveness Analysis Conducted Within Randomised Controlled Trials," PharmacoEconomics, Springer, vol. 32(12), pages 1157-1170, December.
    6. Andrea Gabrio & Alexina J. Mason & Gianluca Baio, 2017. "Handling Missing Data in Within-Trial Cost-Effectiveness Analysis: A Review with Future Recommendations," PharmacoEconomics - Open, Springer, vol. 1(2), pages 79-97, June.
    7. Mohamed El Alili & Johanna M. van Dongen & Jonas L. Esser & Martijn W. Heymans & Maurits W. van Tulder & Judith E. Bosmans, 2022. "A scoping review of statistical methods for trial‐based economic evaluations: The current state of play," Health Economics, John Wiley & Sons, Ltd., vol. 31(12), pages 2680-2699, December.
    8. Borislava Mihaylova & Andrew Briggs & Anthony O'Hagan & Simon G. Thompson, 2011. "Review of statistical methods for analysing healthcare resources and costs," Health Economics, John Wiley & Sons, Ltd., vol. 20(8), pages 897-916, August.

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