A NICE Wind of Change? The What and So What of the NICE Methods Consultation

This blog post summarises some of the more notable changes put forward in NICE’s methods consultation (November – December 2020), and comments on their potential consequences.

This blog post summarises some of the more notable changes put forward in NICE’s methods consultation (November – December 2020), and comments on their potential consequences.

In England and Wales, new treatments are appraised primarily – in relation to cost-effectiveness – by the National Institute for Health and Care Excellence (NICE) to determine whether they should be made available on the National Health Service (NHS). NICE publish and periodically update their guide to the methods of technology appraisal, setting out the key principles of appraisal methodology (Sculpher and Palmer, 2020) which is used as an example by healthcare priority setting bodies internationally. NICE have recently published a consultation on proposed changes to its methods. In this blog we summarise some of the more notable changes put forward for consultation and comment on their potential consequences.

A key proposed change relates to modifiers, which represent factors or circumstances where it is plausible to depart from the principle that health gains have the same value. Currently, NICE applies an explicit and quantitative modifier only for treatments that prolong life to patients that have less than 24 months to live. NICE proposes to replace this with a modifier considering severity of the condition in a broader way, potentially including patients’ quality of life in addition to life extensions. This means that a larger set of interventions – including those for rare diseases – can potentially be appraised against a higher cost-effectiveness threshold (up to £50,000 per QALY gained, compared to the baseline threshold of £20-30,000). However, without a clear proposal as to how it will be operationalised, it is not currently possible to assess its potential impact. In recent years, there has been substantial debate on the rationale for paying a premium for treatments for severe conditions, which has led to related priority setting approaches being applied in some countries (Zhang and Garau, 2020), as well as being proposed by NICE in 2014 as part of the value-based assessment consultation (referred to as ‘burden of illness’) (NICE, 2014). The case for change is there, but there is a need for a clear and detailed implementation proposal to be developed. A fully quantitative version of multi-criteria decision analysis (MCDA) is ruled out in the proposal. More deliberative versions might be considered, which can offer explicit and systematic ways (Garau and Devlin, 2017) to potentially incorporate multiple modifiers into decision-making compared to the current NICE approach.

Another key change included in the consultation is related to the discount rate for both costs and health effects, going from 3.5%, as stated in the current methods guide, to 1.5%. The use of lower discount rates implies an increase in the importance that NICE gives to future costs and health benefits. The effect of a lower discount rate, as proposed in the consultation, would be of relevance for technologies that have high upfront costs and benefits that occur in the future and/or may last a lifetime. Notable examples are one-off therapies, vaccination programs and potentially curative technologies such as some advanced therapy medicinal products (ATMPs). A lower discount rate will decrease their relative disadvantage compared to technologies for which costs are more uniformly spread along the patients’ pathway, or technologies where costs are more likely to increase in future years (e.g. degenerative diseases). Given the potential impact of this change, NICE cautiously indicates that there are a number of “policy and affordability challenges”, and they will “need to be considered separately before any change could be implemented”.    

Tackling uncertainty remains one of the most cumbersome challenges for NICE. The consulting document reflects NICE’s viewpoint that “a greater degree of uncertainty and risk should be accepted in defined circumstances, including: conditions for which it is recognised that evidence generation is complex and difficult, such as rare diseases; innovative technologies; technologies that provide large benefits; when the uncertainty and risks can be monitored and controlled, such as in a managed access arrangement.” Moving towards greater acceptance of decision uncertainty is an important step to more explicitly balance the expected net benefit of making a decision under uncertainty, against the expected net benefit of that decision made with less uncertainty. Indeed, uncertainty is a feature of evidence and understanding, and should therefore be appropriately characterised, presented and considered as part of every appraisal.

However, the proposed changes in the NICE methods on the usage of Expected Value of Perfect Information (EVPI) to inform decisions on further research do not seem to clearly consider that reducing uncertainty by collecting more information has a cost of its own. Such costs include the additional data collection itself, which may fall on the manufacturer but also likely on the NHS or the patients themselves. In addition, there is the potential cost of delaying patient access, for example in the case of negative or ‘only in research’ decisions. The literature on ‘value of information’ analysis is unequivocal in stating that “it should be used to guide decision making under uncertainty with the understanding that a positive EVPI is a necessary, but not sufficient, prerequisite to decide that further research is worthwhile” and “where the expected cost of research exceed the population EVPI, further research is not worthwhile” (Fenwick et al., 2020). Simply put, decision uncertainty should be accepted when the cost of resolving that uncertainty is larger than the cost of accepting the consequences of a potentially ‘wrong’ decision with current evidence.  

The case for change made to improve the way uncertainty is characterised, presented, and handled in appraisals allows decision-makers to make real progress in specific scenarios where relatively large decision uncertainty is unavoidable, and where decision making has thus far been most severely hampered by this. More is needed, however, to assure that any formal usage of value of information analysis is done in accordance with best practice (Fenwick et al., 2020; Rothery et al., 2020).

No major change is suggested by NICE in relation to the reference-case guidance for measuring and valuing health-related quality of life (HRQOL). NICE endorses their initial position statement recommending the use of the EQ-5D-3L tool, with potential use of the EQ-5D-5L questionnaire, provided that results are mapped to 3L. NICE now recommends changing “the preferred method for mapping from EQ-5D-5L to EQ-5D-3L to the tool developed by the NICE Decision Support Unit”, replacing the current preferred mapping method by van Hout et al., 2012.The use of the EQ-5D-3L tool, with a mapping 5L to 3L, is a temporary fix to the 3L vs 5L conflict, since the HRQOL task and finish (T&F) group report states that a second UK valuation for EQ-5D-5L “is expected to begin in 2021. Once complete, NICE will review its methods guidance on EQ-5D valuation and mapping.”    

In relation to the HRQOL: non-reference-case guidance, NICE introduces a long-awaited hierarchy of “preferred health-related quality-of-life methods for when the reference-case measure (EQ 5D) is not available or is not appropriate”, as shown in Figure 1.

FIGURE 1. HIERARCHY OF PREFERRED HEALTH-RELATED QUALITY OF LIFE METHODS

Source: HRQOL T&F Group Report

Although the guidance has not changed substantially, the hierarchy makes it clearer that, when EQ-5D is not appropriate, alternative generic preference-based measures should be used over condition-specific preference-based measures. There is also clearer advice around the use of vignettes. Elsewhere, the consultation falls short on proposing any significant changes for the measurement of HRQOL for carers and younger populations.

NICE partially addresses the challenges to new technologies on specific treatment types. The technology specific T&F group reviewed key challenges for innovative technologies such as ATMPs and histology-independent cancer treatments (HIT). NICE identified generally applicable methods improvements that are relevant to these challenging topics, rather than considering specific alterations for each. For instance, including scenario analyses to explore the effects of assumptions in cases where there is uncertainty about long-term benefits; consideration of cure-proportion modelling as an option; and using a net health benefit approach to present uncertainty. Combined with the existing opportunities for leveraging post-authorisation data collection models and innovative payment models, e.g. through the Cancer Drugs Fund, NICE has been shown to be forward-thinking, which is critical considering the growing pipeline for such therapies. However, further methodological and policy hurdles will need to be overcome in England and beyond. These are described and recommended on in our recently published report (Rodes Sanchez, Henderson and Steuten, 2020).

In conclusion, NICE proposed changes to address a number of current challenges of treatment evaluation. However, there are several important decisions yet to be made to ensure that these changes can be implemented. While we welcome most proposed changes, we look forward to seeing this work progress in 2021 when a new consultation will be published.

Citation

Schmidler, G.D. and Rothery, C., 2020. Value of Information Analysis for Research Decisions—An Introduction: Report 1 of the ISPOR Value of Information Analysis Emerging Good Practices Task Force. Value in Health, 23(2), pp.139–150. 10.1016/j.jval.2020.01.001.

Garau, M. and Devlin, N.J., 2017. Using MCDA as a decision aid in Health Technology Appraisal for coverage decisions: opportunities, challenges and unresolved questions. In: K. Marsh, M. Goetghebeur, P. Thokala and R. Baltussen, eds., Multi-Criteria Decision Analysis to Support Healthcare Decisions. Springer.

van Hout, B., Janssen, M.F., Feng, Y.-S., Kohlmann, T., Busschbach, J., Golicki, D., Lloyd, A., Scalone, L., Kind, P. and Pickard, A.S., 2012. Interim Scoring for the EQ-5D-5L: Mapping the EQ-5D-5L to EQ-5D-3L Value Sets. Value in Health, 15(5), pp.708–715. 10.1016/j.jval.2012.02.008.

NICE, 2014. Value Based Assessment of Health Technologies. [online] London: NICE. Available at: https://www.nice.org.uk/Media/Default/About/what-we-do/NICE-guidance/NICE-technology-appraisals/VBA-TA-Methods-Guide-for-Consultation.pdf

Rodes Sanchez, M., Henderson, N. and Steuten, L., 2020. Bridging the Gap: Pathways for Regulatory and Health Technology Assessment of Histology Independent Therapies. OHE Consulting Report. London: Office of Health Economics.

Sculpher, M. and Palmer, S., 2020. After 20 years of using economic evaluation, should NICE be considered a methods innovator? PharmacoEconomics, 38(3), pp.247-257.

Rothery, C., Strong, M., Koffijberg, H. (Erik), Basu, A., Ghabri, S., Knies, S., Murray, J.F., Sanders Schmidler, G.D., Steuten, L. and Fenwick, E., 2020. Value of Information Analytical Methods: Report 2 of the ISPOR Value of Information Analysis Emerging Good Practices Task Force. Value in Health, 23(3), pp.277–286. 10.1016/j.jval.2020.01.004.

Zhang, K. and Garau, M., 2020. International Cost-Effectiveness Thresholds and Modifiers for HTA Decision Making. OHE Consulting Report. London: Office of Health Economics. 10.1007/978-3-319-47540-0_1410.1007/978

–3–319–47540

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Related Research

Rodes Sanchez, M., Henderson, N. and Steuten, L., 2020. Bridging the Gap: Pathways for Regulatory and Health Technology Assessment of Histology Independent Therapies. OHE Consulting Report. London: Office of Health Economics.

Zhang, K. and Garau, M., 2020. International Cost-Effectiveness Thresholds and Modifiers for HTA Decision Making. OHE Consulting Report. London: Office of Health Economics.