1 Personal Social Services Research Unit, London School of Economics and Political Science, London, UK.
2Professor of Social Policy, Personal Social Services Research Unit, London School of Economics and Political Science, London, UK.
Health Econ Policy Law. 2014 Apr;9(2):119-41. doi: 10.1017/S1744133113000030. Epub 2013 May 21.
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.
英国国家卫生与临床优化研究所(NICE)就新医疗技术的资金投入和使用问题向英格兰和威尔士的国民保健服务体系(NHS)提供指导。本研究旨在调查证据、流程和背景因素对 NICE 2004-2009 年决策的影响。创建了一份 NICE 制药技术决策数据集,其中包含了从已发表信息中提取的 32 个变量。使用三分类结果变量来定义决策,即“推荐”、“限制”或“不推荐”某项技术。采用多变量逻辑回归分析,评估了各解释变量对 NICE 决策的相对贡献。共分析了 65 项技术评估(118 项技术)。其中,27%的技术被推荐,58%的技术被限制,14%的技术不被 NICE 推荐用于 NHS 资金投入。多变量模型显示,NICE 结果与四个变量之间存在显著关联(p ⩽ 0.10):(i)被评估技术的临床试验主要终点具有统计学优势;(ii)增量成本效益比(ICER);(iii)同一评估中评估的药品数量;以及(iv)评估年份。结果证实了采用全面和多变量方法来理解 NICE 决策制定的价值。确定了影响 NICE 决策的新因素,包括临床优势的影响以及流程和社会经济因素的影响。