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The influence of cost-effectiveness and other factors on NICE decisions

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Author Info

  • Helen Dakin

    (Health Economics Research Centre, University of Oxford, UK)

  • Nancy Devlin

    (Office of Health Economics, London, UK)

  • Yan Feng

    (Office of Health Economics, London, UK)

  • Nigel Rice

    (Centre for Health Economics and Department of Economics and Related Studies, University of York, UK)

  • Phill O’Neill

    (Office of Health Economics, London, UK)

  • David Parkin

    (Department of Primary Care and Public Health Sciences, King’s College London, UK)

Abstract

Background: The National Institute for Health and Care Excellence (NICE) emphasises that cost-effectiveness is not the only consideration in health technology appraisal and is increasingly explicit about other factors considered relevant. Observing NICE decisions and the evidence considered in each appraisal allows us to ‘reveal’ its implicit weights. Objectives: This study aims to investigate the influence of cost-effectiveness and other factors on NICE decisions and to investigate whether NICE’s decision-making has changed through time. Methods: We build on and extend the modelling approaches in Devlin and Parkin (2004) and Dakin et al (2006). We model NICE’s decisions as binary choices: i.e. recommendations for or against use of a healthcare technology in a specific patient group. Independent variables comprised: the clinical and economic evidence regarding that technology; the characteristics of the patients, disease or treatment; and contextual factors affecting the conduct of health technology appraisal. Data on all NICE decisions published by December 2011 were obtained from HTAinSite [www.htainsite.com]. Results: Cost-effectiveness alone correctly predicted 82% of decisions; few other variables were significant and alternative model specifications led to very small variations in model performance. The odds of a positive NICE recommendation differed significantly between musculoskeletal disease, respiratory disease, cancer and other conditions. The accuracy with which the model predicted NICE recommendations was slightly improved by allowing for end of life criteria, uncertainty, publication date, clinical evidence, only treatment, paediatric population, patient group evidence, appraisal process, orphan status, innovation and use of probabilistic sensitivity analysis, although these variables were not statistically significant. Although there was a non-significant trend towards more recent decisions having a higher chance of a positive recommendation, there is currently no evidence that the threshold has changed over time. The model with highest prediction accuracy suggested that a technology costing £40,000 per quality-adjusted life-year (QALY) would have a 50% chance of NICE rejection (75% at £52,000/QALY; 25% at £27,000/QALY). Discussion: Past NICE decisions appear to have been based on a higher threshold than the £20,000- £30,000/QALY range that is explicitly stated. However, this finding may reflect consideration of other factors that drive a small number of NICE decisions or cannot be easily quantified.

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File URL: http://www.york.ac.uk/media/che/documents/papers/researchpapers/CHERP93_cost-effectiveness_NICE_decisions.pdf
File Function: First version, 2013
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Bibliographic Info

Paper provided by Centre for Health Economics, University of York in its series Working Papers with number 093cherp.

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Length: 32 pages
Date of creation: Nov 2013
Date of revision:
Handle: RePEc:chy:respap:93cherp

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Related research

Keywords: Health technology assessment; implicit weights; cost-effectiveness; National Institute for Health and Care Excellence (NICE); logistic regression;

This paper has been announced in the following NEP Reports:

References

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  1. John B. Carlin & John C. Galati & Patrick Royston, 2008. "A new framework for managing and analyzing multiply imputed data in Stata," Stata Journal, StataCorp LP, vol. 8(1), pages 49-67, February.
  2. Culyer, Anthony J., 2006. "NICE's use of cost effectiveness as an exemplar of a deliberative process," Health Economics, Policy and Law, Cambridge University Press, vol. 1(03), pages 299-318, July.
  3. Rosen, Sherwin, 1974. "Hedonic Prices and Implicit Markets: Product Differentiation in Pure Competition," Journal of Political Economy, University of Chicago Press, vol. 82(1), pages 34-55, Jan.-Feb..
  4. Appleby, John & Devlin, Nancy & Parkin, David & Buxton, Martin & Chalkidou, Kalipso, 2009. "Searching for cost effectiveness thresholds in the NHS," Health Policy, Elsevier, vol. 91(3), pages 239-245, August.
  5. Dakin, Helen Angela & Devlin, Nancy J. & Odeyemi, Isaac A.O., 2006. ""Yes", "No" or "Yes, but"? Multinomial modelling of NICE decision-making," Health Policy, Elsevier, vol. 77(3), pages 352-367, August.
  6. Andrew Briggs & Paul Fenn, 1998. "Confidence intervals or surfaces? Uncertainty on the cost-effectiveness plane," Health Economics, John Wiley & Sons, Ltd., vol. 7(8), pages 723-740.
  7. Nancy Devlin & David Parkin, 2004. "Does NICE have a cost-effectiveness threshold and what other factors influence its decisions? A binary choice analysis," Health Economics, John Wiley & Sons, Ltd., vol. 13(5), pages 437-452.
  8. Warren G. Linley & Dyfrig A. Hughes, 2012. "Reimbursement Decisions of the All Wales Medicines Strategy Group: Influence of Policy and Clinical and Economic Factors," PharmacoEconomics, Springer Healthcare | Adis, vol. 30(9), pages 779-794.
  9. Helen Mason & Michael Jones-Lee & Cam Donaldson, 2009. "Modelling the monetary value of a QALY: a new approach based on UK data," Health Economics, John Wiley & Sons, Ltd., vol. 18(8), pages 933-950.
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