Healthcare decision makers, such as the National Institute of Health and Care Excellence in the UK, use cost-effectiveness analysis and modelling to compare the costs and effects of disease management strategies. These analyses rely on limited evidence and decisions are often uncertain. Value of information (VoI) analysis quantifies the monetary value to decision makers of gathering further evidence. VoI requires nested Monte Carlo simulation to estimate the uncertain benefits of further research, which is computationally impractical for all but the simplest of cost-effectiveness models. Model regression and approximation approaches, including Gaussian processes, generalised additive models, and integrated nested Laplace approximation (INLA), have come into use as short-cut approaches to estimating VoI. However, these approaches may be unreliable for realistic models.

In my talk, I will explain these issues in greater detail and highlight problems with model regression and approximation when applied to realistic economic models. As an alternative, I will present adaptations of Multilevel and Quasi Monte Carlo sampling schemes from computational finance to the estimation of VoI. These achieve the same accuracy and precision of standard Monte Carlo with lower computational cost by minimising the variance and bias of their VoI estimators. I will apply both methods to example cost-effectiveness models, including a model used in the UK national guidelines on directly acting oral anticoagulants (DOACs) for prevention of stroke in atrial fibrillation. Results will be compared with estimates from model regression and approximation.