"Yes", "No" or "Yes, but"? Multinomial modelling of NICE decision-making

Health Policy. 2006 Aug;77(3):352-67. doi: 10.1016/j.healthpol.2005.08.008. Epub 2005 Oct 5.


The National Institute for Health and Clinical Excellence (NICE) issues mandatory guidance on health technologies to the UK NHS, based on clinical evidence, cost-effectiveness and other considerations. However, the exact factors considered, their relative importance and tradeoffs between them are not made explicit. Previous research modelled NICE decisions as a binary choice (accept/reject) dependent on cost-effectiveness, amongst other variables. This paper proposes and tests an alternative model of decision-making that may better represent the "yes, but..." nature of many NICE decisions. Decisions were categorised as "recommended for routine use", "recommended for restricted use" or "not recommended". The NICE appraisal process was modelled as a single decision between the three categories. Multinomial logistic regression techniques were used to evaluate the impact of: quantity/quality of clinical evidence; cost-effectiveness; decision date; existence of alternative treatments; budget impact; technology type. Results suggest that interventions supported by more randomised trials are more likely to be recommended and endorsed for routine use. Higher cost-effectiveness ratios increased the likelihood of interventions being rejected rather than recommended for restricted use but did not significantly affect the decision between routine and restricted use. Pharmaceuticals, interventions appraised early in the NICE programme and those with more systematic reviews were also less likely to be rejected, while patient group submissions made a recommendation for routine rather than restricted use more likely. The presence of factors affecting the decision between routine and restricted use but not that between routine use and rejection suggests that modelling these three outcomes reflects NICE decision-making more closely than binary-choice analyses.

MeSH terms

  • Decision Support Techniques*
  • Randomized Controlled Trials as Topic
  • State Medicine
  • Technology Assessment, Biomedical / organization & administration*
  • United Kingdom