Objectives: The aim of this study was to describe the evolution of a cost-utility model used to inform the UK National Institute for Health and Clinical Excellence's (NICE) most recent decisions on the cost-utility of drug treatments for Alzheimer's disease (AD), and to explore the impact of structural assumptions on the cost-utility results.
Methods: Changes informed by noted limitations of the decision model used in NICE's previous decisions (in 2006) were made cumulatively to the original decision model for donepezil compared with best supportive care (for patients with mild to moderate AD). Deterministic and probabilistic analyses were undertaken for each cumulative change of the model. The expected value of perfect information (EVPI) of parameter estimates and structural assumptions was also calculated.
Results: Cumulative changes to the decision model highlighted how the results of the original model (incremental cost-effectiveness ratio of £81,000 per quality-adjusted life-year gained) related to those of the new model (where donepezil was estimated to be cost-saving), mainly due to uncertainty in the incremental cost of donepezil treatment over best supportive care (ranging from -£600 to £3,000 per patient). The partial EVPI analysis reflected this finding where further information on treatment discontinuations and cost parameter estimates were shown to be valuable in terms of reducing decision uncertainty.
Conclusions: Assessing the evolution of the cost-utility model helped to identify and explore structural differences between cohort-based models and is likely to be useful for decision models in other disease areas. This approach makes the structural uncertainty explicit, forcing decision makers to address structural uncertainty in addition to parameter uncertainty.