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An Analysis of NICE’s ‘Restricted’ (or ‘Optimized’) Decisions

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  • Phill O’Neill
  • Nancy Devlin

Abstract

Background: A common way of describing UK National Institute for Health and Clinical Excellence (NICE) decisions is to distinguish between cases where NICE recommended use of a healthcare technology by all relevant patients (‘yes’); those where it did not recommend use (‘no’); and those where its decisions are a mixture of ‘yes’ to some patient subgroups, and ‘no’ to others. Over half of NICE’s decisions are of this mixed type, which involve restricting (or‘optimizing’) patient use in some way. Objective: To report an attempt to develop a robust and defensible means of measuring and describing the degree of patient access in mixed NICE decisions. Methods: A list of mixed decisions made from 2006 to the end of 2009 was identified using HTAinSite™. The following calculation was used: M= (p/P) × 100, where M is a measure of the level of patient access (0=no access, 100=full access), P is the set of patients considered in the guidance as Potential candidates for treatment (given the licensed use and the scope of NICE’s appraisal), and p is a subset of those patients, for whom NICE did recommend treatment.Mcan be estimated either for a specific product or for a group of technologies (Multiple Technology Appraisals). Both productspecific and overall M were estimated, using estimates of p obtained from NICE costing templates. These data are subject to some important limitations, so the results should be regarded as illustrative. Results: Of the 69 medicines that have received a mixed decision since January 2006, 34 included details that allowed the estimation of M. Of these 34 decisions, 24 (71%) had a product-specific M ≤50, 16 (47%) M ≤25 and 11 (32%) M ≤10. That is, in just under three-quarters of the mixed decisions for which P and p were available, NICE recommended use for less than half of patients for whom the medicine is licensed, and in nearly one-third of these sorts of decisions, NICE recommended use in ≤10% of potential patients. The estimates of M for groups of technologies provide a slightly different picture: for example, grouped M was ≤10 in >20% of decisions. Conclusions: The measure of patient access, M, proposed here has the potential to provide a more informative way of reporting all NICE decisions, particularly ‘restricted’ (or ‘optimized’) decisions. Copyright Adis Data Information BV 2010

Suggested Citation

  • Phill O’Neill & Nancy Devlin, 2010. "An Analysis of NICE’s ‘Restricted’ (or ‘Optimized’) Decisions," PharmacoEconomics, Springer, vol. 28(11), pages 987-993, November.
    • Handle: RePEc:spr:pharme:v:28:y:2010:i:11:p:987-993
    DOI: 10.2165/11536970-000000000-00000
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    References listed on IDEAS

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    1. 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, May.
    2. 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.
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    1. Fischer, Katharina E. & Rogowski, Wolf H. & Leidl, Reiner & Stollenwerk, Björn, 2013. "Transparency vs. closed-door policy: Do process characteristics have an impact on the outcomes of coverage decisions? A statistical analysis," Health Policy, Elsevier, vol. 112(3), pages 187-196.
    2. Fischer, Katharina Elisabeth, 2012. "A systematic review of coverage decision-making on health technologies—Evidence from the real world," Health Policy, Elsevier, vol. 107(2), pages 218-230.

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