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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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Author Info
Paul C. Lambert (Centre for Biostatistics and Genetic Epidemiology, Department of Health Sciences, University of Leicester, UK)
Lucinda J. Billingham (Cancer Research UK Clinical Trials Unit, University of Birmingham, UK)
Nicola J. Cooper (Centre for Biostatistics and Genetic Epidemiology, Department of Health Sciences, University of Leicester, UK)
Alex J. Sutton (Centre for Biostatistics and Genetic Epidemiology, Department of Health Sciences, University of Leicester, UK)
Keith R. Abrams (Centre for Biostatistics and Genetic Epidemiology, Department of Health Sciences, University of Leicester, UK)
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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File URL: http://hdl.handle.net/10.1002/hec.1243
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Publisher Info
Article provided by John Wiley & Sons, Ltd. in its journal Health Economics.

Volume (Year): 17 (2008)
Issue (Month): 1 ()
Pages: 67-81
Download reference. The following formats are available: HTML, plain text, BibTeX, RIS (EndNote), ReDIF
Handle: RePEc:wly:hlthec:v:17:y:2008:i:1:p:67-81

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Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/5749

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  1. Andrew Briggs & Richard Nixon & Simon Dixon & Simon Thompson, 2005. "Parametric modelling of cost data: some simulation evidence," Health Economics, John Wiley & Sons, Ltd., vol. 14(4), pages 421-428. [Downloadable!]
  2. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika van der Linde, 2002. "Bayesian measures of model complexity and fit," Journal Of The Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639. [Downloadable!] (restricted)
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This page was last updated on 2008-9-24.


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