Cookies on this website
We use cookies to ensure that we give you the best experience on our website. If you click 'Continue' we'll assume that you are happy to receive all cookies and you won't see this message again. Click 'Find out more' for information on how to change your cookie settings.

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 but not the weight attached to each. The objective of this study is to investigate the influence of cost-effectiveness and other factors on NICE decisions and whether NICE's decision-making has changed over time. We model NICE's decisions as binary choices for or against a health care technology in a specific patient group. Independent variables comprised of the following: clinical and economic evidence; characteristics of patients, disease or treatment; and contextual factors potentially affecting decision-making. Data on all NICE decisions published by December 2011 were obtained from HTAinSite [www.htainsite.com]. Cost-effectiveness alone correctly predicted 82% of decisions; few other variables were significant and alternative model specifications had similar performance. There was no evidence that the threshold has changed significantly over time. The model with highest prediction accuracy suggested that technologies costing £40 000 per quality-adjusted life-year (QALY) have a 50% chance of NICE rejection (75% at £52 000/QALY; 25% at £27 000/QALY). Past NICE decisions appear to have been based on a higher threshold than £20 000-£30 000/QALY. However, this may reflect consideration of other factors that cannot be easily quantified. © 2014 The Authors. Health Economics published by John Wiley & Sons Ltd.

Original publication

DOI

10.1002/hec.3086

Type

Journal

Health Econ

Publication Date

23/09/2014

Keywords

National Institute for Health and Care Excellence (NICE), cost-effectiveness, health technology assessment, implicit weights, logistic regression