Efficient Monte Carlo Estimation of the Expected Value of Sample Information using Moment Matching

TL;DR

This paper introduces a practical Monte Carlo approximation method for calculating the Expected Value of Sample Information (EVSI) in health economics, reducing computational complexity by using moment matching techniques.

Contribution

The paper proposes a novel EVSI approximation method based on moment matching and existing probabilistic sensitivity analysis simulations, improving computational efficiency.

Findings

Method successfully applied to a health economic example

Reduces computational burden compared to nested simulations

Fits well with existing probabilistic sensitivity analysis workflows

Abstract

The Expected Value of Sample Information (EVSI) is used to calculate the economic value of a new research strategy. While this value would be important to both researchers and funders, there are very few practical applications of the EVSI. In the main, this is due to computational difficulties associated with calculating the EVSI in practical health economic models using nested simulations. We present an approximation method for the EVSI that is based on estimating the distribution of the posterior mean of the incremental net benefit across all the possible future samples, known as the distribution of the preposterior mean. Specifically, we suggest that this distribution is estimated using moment matching coupled with simulations that are available for probabilistic sensitivity analysis, which is typically mandatory in health economic evaluation. We demonstrate that this method is successful using an example that has previously been applied to other EVSI approximation methods. We then conclude by discussing how our method fits in with other recent additions to the literature that detail approximation methods for the EVSI.