IDEAS home Printed from https://ideas.repec.org/p/lee/wpaper/1308.html
   My bibliography  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

Author

Listed:
  • 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))

Abstract

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.

Suggested Citation

  • Samer A Kharroubi & Richard Edlin & David Meads & Chantelle Browne & Julia Brown & Christopher McCabe, 2013. "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," Working Papers 1308, Academic Unit of Health Economics, Leeds Institute of Health Sciences, University of Leeds.
  • Handle: RePEc:lee:wpaper:1308
    as

    Download full text from publisher

    File URL: http://medhealth.leeds.ac.uk/download/343/auhe_wp13_08
    File Function: First version, 2013
    Download Restriction: no

    References listed on IDEAS

    as
    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)

    More about this item

    Keywords

    Bayesian methods; EQ-5D; Multiple Myeloma; Quality of Life; mapping; Cost-utility analysis; regression modelling.;

    JEL classification:

    • I1 - Health, Education, and Welfare - - Health

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. 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). General contact details of provider: http://edirc.repec.org/data/heleeuk.html .

    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 CitEc recognized a reference but did not link an item in RePEc 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 RePEc Author Service 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.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.