Calculating the Expected Value of Sample Information Using Efficient Nested Monte Carlo: A Tutorial

Abstract

Objective: The expected value of sample information (EVSI) quantifies the economic benefit of reducing uncertainty in a health economic model by collecting additional information. This has the potential to improve the allocation of research budgets. Despite this, practical EVSI evaluations are limited partly due to the computational cost of estimating this value using the gold-standard nested simulation methods. Recently, however, Heath et al. developed an estimation procedure that reduces the number of simulations required for this gold-standard calculation. Up to this point, this new method has been presented in purely technical terms.

Study design: This study presents the practical application of this new method to aid its implementation. We use a worked example to illustrate the key steps of the EVSI estimation procedure before discussing its optimal implementation using a practical health economic model.

Methods: The worked example is based on a three-parameter linear health economic model. The more realistic model evaluates the cost-effectiveness of a new chemotherapy treatment, which aims to reduce the number of side effects experienced by patients. We use a Markov model structure to evaluate the health economic profile of experiencing side effects.

Results: This EVSI estimation method offers accurate estimation within a feasible computation time, seconds compared to days, even for more complex model structures. The EVSI estimation is more accurate if a greater number of nested samples are used, even for a fixed computational cost.

Conclusions: This new method reduces the computational cost of estimating the EVSI by nested simulation.

Keywords: health economic evaluations; probabilistic sensitivity analysis; sample information; trial design; value of information.