Decision making by NICE: examining the influences of evidence, process and context
Please note: this is a legacy publication from CPEC (formely PSSRU at LSE).Health Economics, Policy and Law 9 2 119-141
Available online: 21 May 2013
Abstract
The National Institute for Health and Clinical Excellence (NICE) provides guidance to the National Health Service (NHS) in England and Wales on funding and use of new technologies. This study examined the impact of evidence, process and context factors on NICE decisions in 2004–2009. A data set of NICE decisions pertaining to pharmaceutical technologies was created, including 32 variables extracted from published information. A three-category outcome variable was used, defined as the decision to ‘recommend’, ‘restrict’ or ‘not recommend’ a technology. With multinomial logistic regression, the relative contribution of explanatory variables on NICE decisions was assessed. A total of 65 technology appraisals (118 technologies) were analysed. Of the technologies, 27% were recommended, 58% were restricted and 14% were not recommended by NICE for NHS funding. The multinomial model showed significant associations (p ? 0.10) between NICE outcome and four variables: (i) demonstration of statistical superiority of the primary endpoint in clinical trials by the appraised technology; (ii) the incremental cost-effectiveness ratio (ICER); (iii) the number of pharmaceuticals appraised within the same appraisal; and (iv) the appraisal year. Results confirm the value of a comprehensive and multivariate approach to understanding NICE decision making. New factors affecting NICE decision making were identified, including the effect of clinical superiority, and the effect of process and socio-economic factors.
The National Institute for Health and Clinical Excellence (NICE) provides guidance to the National Health Service (NHS) in England and Wales on funding and use of new technologies. This study examined the impact of evidence, process and context factors on NICE decisions in 2004–2009. A data set of NICE decisions pertaining to pharmaceutical technologies was created, including 32 variables extracted from published information. A three-category outcome variable was used, defined as the decision to ‘recommend’, ‘restrict’ or ‘not recommend’ a technology. With multinomial logistic regression, the relative contribution of explanatory variables on NICE decisions was assessed. A total of 65 technology appraisals (118 technologies) were analysed. Of the technologies, 27% were recommended, 58% were restricted and 14% were not recommended by NICE for NHS funding. The multinomial model showed significant associations (p ? 0.10) between NICE outcome and four variables: (i) demonstration of statistical superiority of the primary endpoint in clinical trials by the appraised technology; (ii) the incremental cost-effectiveness ratio (ICER); (iii) the number of pharmaceuticals appraised within the same appraisal; and (iv) the appraisal year. Results confirm the value of a comprehensive and multivariate approach to understanding NICE decision making. New factors affecting NICE decision making were identified, including the effect of clinical superiority, and the effect of process and socio-economic factors.