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Description of an individual patient methodology for calculating the cost-effectiveness of treatments for osteoporosis in women

Author

Listed:
  • M D Stevenson

    (School of Health and Related Research, University of Sheffield)

  • J E Brazier

    (School of Health and Related Research, University of Sheffield)

  • N W Calvert

    (School of Health and Related Research, University of Sheffield)

  • M Lloyd-Jones

    (School of Health and Related Research, University of Sheffield)

  • J E Oakley

    (University of Sheffield)

  • J A Kanis

    (WHO Collaborating Centre for Metabolic Bone Diseases, University of Sheffield Medical School)

Abstract

Models of the cost-effectiveness of pharmaceutical interventions for the treatment of osteoporosis have traditionally adopted cohort-based approaches. We present a transition-state model to simulate the experience of individual patients, allowing the full patient history and residential status to influence the probabilities of future fractures at the hip, spine, wrist or proximal humerus. Alongside epidemiological data, we used systematic literature reviews of costs, utilities and efficacy to populate the model for a UK setting. We established a statistical relationship between the inputs and outputs of the individual patient model creating a near instantaneous emulation of the individual patient model. We undertook extensive sensitivity analyses to analyse the uncertainty in the estimated incremental cost per quality-adjusted life year due to uncertainty in the efficacy of the drugs. We provide illustrative results accompanied by individual and multi-interventional cost-effectiveness acceptability curves.

Suggested Citation

  • M D Stevenson & J E Brazier & N W Calvert & M Lloyd-Jones & J E Oakley & J A Kanis, 2005. "Description of an individual patient methodology for calculating the cost-effectiveness of treatments for osteoporosis in women," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(2), pages 214-221, February.
    • Handle: RePEc:pal:jorsoc:v:56:y:2005:i:2:d:10.1057_palgrave.jors.2601903
    DOI: 10.1057/palgrave.jors.2601903
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    References listed on IDEAS

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    1. Elisabeth Fenwick & Karl Claxton & Mark Sculpher, 2001. "Representing uncertainty: the role of cost‐effectiveness acceptability curves," Health Economics, John Wiley & Sons, Ltd., vol. 10(8), pages 779-787, December.
    2. Birch, Stephen & Gafni, Amiram, 1992. "Cost effectiveness/utility analyses : Do current decision rules lead us to where we want to be?," Journal of Health Economics, Elsevier, vol. 11(3), pages 279-296, October.
    3. Aaron A. Stinnett & A. David Paltiel, 1997. "Estimating CE Ratios under Second-order Uncertainty," Medical Decision Making, , vol. 17(4), pages 483-489, October.
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    Cited by:

    1. Anthony O'Hagan & Matt Stevenson & Jason Madan, 2007. "Monte Carlo probabilistic sensitivity analysis for patient level simulation models: efficient estimation of mean and variance using ANOVA," Health Economics, John Wiley & Sons, Ltd., vol. 16(10), pages 1009-1023.
    2. Alan Brennan & Stephen E. Chick & Ruth Davies, 2006. "A taxonomy of model structures for economic evaluation of health technologies," Health Economics, John Wiley & Sons, Ltd., vol. 15(12), pages 1295-1310, December.
    3. H Pilgrim & P Tappenden & J Chilcott & M Bending & P Trueman & A Shorthouse & J Tappenden, 2009. "The costs and benefits of bowel cancer service developments using discrete event simulation," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(10), pages 1305-1314, October.

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