Abstract
ObjectivesDecision makers adopt health technologies based on health economic models that are subject to uncertainty. In an ideal world, these models parameterize all uncertainties and reflect them in the cost-effectiveness probability and risk associated with the adoption. In practice, uncertainty assessment is often incomplete, potentially leading to suboptimal reimbursement recommendations and risk management. This study examines the feasibility of comprehensive uncertainty assessment in health economic models. MethodsA state transition model on peripheral arterial disease treatment was used as a case study. Uncertainties were identified and added to the probabilistic sensitivity analysis if possible. Parameter distributions were obtained by expert elicitation, and structural uncertainties were either parameterized or explored in scenario analyses, which were model averaged. ResultsA truly comprehensive uncertainty assessment, parameterizing all uncertainty, could not be achieved. Expert elicitation informed 8 effectiveness, utility, and cost parameters. Uncertainties were parameterized or explored in scenario analyses and with model averaging. Barriers included time and resource constraints, also of clinical experts, and lacking guidance regarding some aspects of expert elicitation, evidence aggregation, and handling of structural uncertainty. The team’s multidisciplinary expertise and existing literature and tools were facilitators. ConclusionsWhile comprehensive uncertainty assessment may not be attainable, improvements in uncertainty assessment in general are no doubt desirable. This requires the development of detailed guidance and hands-on tutorials for methods of uncertainty assessment, in particular aspects of expert elicitation, evidence aggregation, and handling of structural uncertainty. The issue of benefits of uncertainty assessment versus time and resources needed remains unclear.
Highlights
Health economic models are frequently used to estimate the cost-effectiveness of a healthcare technology compared to its comparator(s), to quantify uncertainty in the cost-effectiveness estimate and risk associated with the adoption decision, and to indicate areas of further research.[1]
The steps of uncertainty assessment undertaken in this case study, the expertise of people involved, as well as observed barriers and facilitators are presented in Table 2 and described in the following
This article explored the feasibility of comprehensive uncertainty assessment in health economic modeling
Summary
Health economic models are frequently used to estimate the cost-effectiveness of a healthcare technology compared to its comparator(s), to quantify uncertainty in the cost-effectiveness estimate and risk associated with the adoption decision, and to indicate areas of further research.[1]. Health economic models allow for uncertainties to be made explicit and to assess their impact on results. Health economic models identify and parameterize all uncertainties and produce one probabilistic cost-effectiveness estimate that reflects all uncertainty. On this basis, a risk estimate can be obtained, which allows for risk management. A risk estimate can be obtained, which allows for risk management In practice this is rarely the case,[2] leading to potentially suboptimal recommendations and risk management.[3,4]
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