IDEAS home Printed from
MyIDEAS: Log in (now much improved!) to save this paper

Use of Bayesian Markov Chain Monte Carlo Methods to Estimate EQ-5D Utility Scores from Eortic QLQ Data in Myeloma for Use in Cost effectiveness Analysis

Listed author(s):
  • Samer A Kharroubi


    (Department of Mathematics, University of York, York)

  • Richard Edlin

    (School of Population Health, University of Auckland)

  • David Meads

    (Academic Unit of Health Economics, University of Leeds, Leeds)

  • Chantelle Browne

    (Academic Unit of Health Economics, University of Leeds, Leeds)

  • Julia Brown

    (Clinical Trials Research Unit, University of Leeds, Leeds)

  • Christopher McCabe

    (Department of Emergency Medicine, School of Community Based Medicine, University of Alberta, Edmonton (Canada))

Background: Patient Reported Outcome Measures are an important component of the evidence for health technology appraisal. Their incorporation into cost effectiveness analyses (CEA) requires conversion of descriptive information into utilities. This can be done using bespoke utility algorithms. Otherwise, investigators will often estimate indirect utility models for the PROMS using off-the-shelf utility data such as the EQ-5D or SF-6D. Many different modeling strategies are reported in the literature; however, to date there has been limited utilization of Bayesian methods in this context. In this paper we use a large trial dataset containing the EORTC QLQ-C30 with MY20 and the EQ-5D to examine the relative advantage of the Bayesian methods in relation to dealing with missing data, relaxing the assumption of equal variances and characterizing the uncertainty in the model predictions. Methods: Data from a large myeloma trial were used to examine the relationship between scores in each of the 19 domains of the EORTC QLQ-C30/QLQ-MY20 and the EQ-5D utility. Data from 1839 patients was divided 75%/25% between derivation and validation sets. A conventional OLS model, assuming equal variance and a Bayesian model allowing unequal variance were estimated on complete cases. Two further models were estimated using conventional and Bayesian multiple imputation respectively, using the full dataset. Models were compared in terms of data fit, accuracy in model prediction and characterization of uncertainty in model predictions. Conclusions: Mean EQ-5D utility weights can be estimated from the EORTC QLQ-C30/QLQMY20 for use in CEA. Frequentist and Bayesian methods produced effectively identical models. However, the Bayesian models provide distributions describing the uncertainty surrounding the estimated utility values and are thus more suited informing analyses for probabilistic CEA.

If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

File URL:
File Function: First version, 2013
Download Restriction: no

Paper provided by Academic Unit of Health Economics, Leeds Institute of Health Sciences, University of Leeds in its series Working Papers with number 1308.

in new window

Length: 20 pages
Date of creation: 2013
Handle: RePEc:lee:wpaper:1308
Contact details of provider: Phone: Worsley Building, Level 11, Clarendon Way, LEEDS LS2 9NL
Fax: +44 (0) 113 343 3470
Web page:

More information through EDIRC

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:

in new window

  1. Tsuchiya, A & Brazier, J & McColl, E & Parkin, D, 2002. "Deriving preference-based single indices from non-preference based condition-specific instruments: converting AQLQ into EQ5D indices," MPRA Paper 29740, University Library of Munich, Germany.
  2. Christopher McCabe & Richard Edlin & David Meads & Chantelle Brown & Samer Kharroubi, 2013. "Constructing Indirect Utility Models: Some Observations on the Principles and Practice of Mapping to Obtain Health State Utilities," PharmacoEconomics, Springer, vol. 31(8), pages 635-641, August.
Full references (including those not matched with items on IDEAS)

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

When requesting a correction, please mention this item's handle: RePEc:lee:wpaper:1308. See general information about how to correct material in RePEc.

For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Judy Wright)

If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

If references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

Please note that corrections may take a couple of weeks to filter through the various RePEc services.

This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.