Abstract
BackgroundMultiple criteria decision analysis (MCDA) has appeared as a methodology to address limitations of economic evaluation in health technology assessment (HTA), however there are limited empirical evidence from real world applications. The aim of this study is to test in practice a recently developed MCDA methodological framework known as Advance Value Framework (AVF) through a proof-of-concept case study engaging multiple stakeholders.MethodsA multi-attribute value theory methodological process was adopted involving problem structuring, model building, model assessment and model appraisal phases. A facilitated decision analysis modelling approach was used as part of a decision conference with thirteen participants. An expanded scope of the National Institute for Health and Care Excellence (NICE) remit acted as the study setting with the use of supplementary value concerns. Second-line biological treatments were evaluated for metastatic colorectal cancer (mCRC) patients having received prior chemotherapy, including cetuximab monotherapy, panitumumab monotherapy and aflibercept in combination with FOLFIRI chemotherapy. Initially 18 criteria attributes were considered spanning four value domains relating to therapeutic impact, safety profile, innovation level and socioeconomic impact.ResultsNine criteria attributes were finally included. Cetuximab scored the highest overall weighted preference value score of 45.7 out of 100, followed by panitumumab with 42.3, and aflibercept plus FOLFIRI with 14.4. The relative weights of the two most important criteria (overall survival and Grade 4 adverse events) added up to more than the relative weight of all other criteria together (52.1%). Main methodological limitation was the lack of comparative clinical effects across treatments and challenges included the selection of “lower” and “higher” reference levels on criteria attributes, eliciting preferences across attributes where participants had less experience, and ensuring that all attributes possess the right decision theory properties.ConclusionsThis first application of AVF produced transparent rankings for three mCRC treatments based on their value, by assessing an explicit set of evaluation criteria while allowing for the elicitation and construction of participants’ value preferences and their trade-offs. It proved it can aid the evaluation process and value communication of the alternative treatments for the group participants. Further research is needed to optimise its use as part of policy-making.
Highlights
Multiple criteria decision analysis (MCDA) has appeared as a methodology to address limitations of economic evaluation in health technology assessment (HTA), there are limited empirical evidence from real world applications
Nine out of the 18 attributes were removed from the value tree because they were judged from the participants to be nonfundamental for the scope of the exercise, resulting in a value tree of half its original size
In the safety profile cluster, the contraindications attribute was removed and the Grade 3 Adverse Events (AEs) and Grade 4 AEs attributes were proposed to be aggregated into a single attribute; this aggregation required a significant modelling iteration, and due to time constraints it was decided to exclude the Grade 3 AEs attribute and only include Grade 4 AEs for the purpose of the simulation exercise
Summary
The assessment and appraisal of new and expensive medicines by health technology assessment (HTA) bodies, health insurers, and gatekeeper agencies has received considerable attention in recent years, especially in countries with publicly funded health care systems This is a consequence of negative, and sometimes controversial, recommendations on the funding of new medicines due to their high costs. The use of QALYs (quality adjusted life years) as part of economic evaluations in HTA, it is a reasonable measure of health gain, it has been argued as inadequate to express the wider patient and societal perspective This is partially because it does not reflect other dimensions of social value relating to the burden of the disease, the innovation level of interventions and their wider socioeconomic impact [1, 2], acting as an incomplete value metric for cancer treatments and genetic testing [3, 4]. For any decision outcome to be understood and regarded as “rationally-based”, the application of more comprehensive decision-making procedures of an explicit and transparent nature is required
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