Probabilistic sensitivity analysis for NICE technology assessment: not an optional extra

TLDR

NICE recently updated its technology‑assessment guidance to mandate probabilistic sensitivity analysis for all cost‑effectiveness models, beginning with a definition of the decision‑making requirements. This paper contextualises the guidance on uncertainty within NICE’s decision‑making framework, explains its development approach, and argues that the probabilistic sensitivity analysis requirement is justified by the nature of NICE’s decisions. The authors identify the methods and analytical framework that best satisfy these decision‑making requirements, focusing on probabilistic sensitivity analysis. They review the main issues and criticisms raised during and after consultation and highlight methodological challenges in fully characterising decision uncertainty and shaping future research.

Abstract

Recently the National Institute for Clinical Excellence (NICE) updated its methods guidance for technology assessment. One aspect of the new guidance is to require the use of probabilistic sensitivity analysis with all cost-effectiveness models submitted to the Institute. The purpose of this paper is to place the NICE guidance on dealing with uncertainty into a broader context of the requirements for decision making; to explain the general approach that was taken in its development; and to address each of the issues which have been raised in the debate about the role of probabilistic sensitivity analysis in general. The most appropriate starting point for developing guidance is to establish what is required for decision making. On the basis of these requirements, the methods and framework of analysis which can best meet these needs can then be identified. It will be argued that the guidance on dealing with uncertainty and, in particular, the requirement for probabilistic sensitivity analysis, is justified by the requirements of the type of decisions that NICE is asked to make. Given this foundation, the main issues and criticisms raised during and after the consultation process are reviewed. Finally, some of the methodological challenges posed by the need fully to characterise decision uncertainty and to inform the research agenda will be identified and discussed.