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

BACKGROUND

The Expected Value of Sample Information (EVSI) is used to calculate the economic value of a new research strategy. Although this value would be important to both researchers and funders, there are very few practical applications of the EVSI. This is due to computational difficulties associated with calculating the EVSI in practical health economic models using nested simulations.

METHODS

We present an approximation method for the EVSI that is framed in a Bayesian setting and is based on estimating the distribution of the posterior mean of the incremental net benefit across all possible future samples, known as the distribution of the preposterior mean. Specifically, this distribution is estimated using moment matching coupled with simulations that are available for probabilistic sensitivity analysis, which is typically mandatory in health economic evaluations.

RESULTS

This novel approximation method is applied to a health economic model that has previously been used to assess the performance of other EVSI estimators and accurately estimates the EVSI. The computational time for this method is competitive with other methods.

CONCLUSION

We have developed a new calculation method for the EVSI which is computationally efficient and accurate.

LIMITATIONS

This novel method relies on some additional simulation so can be expensive in models with a large computational cost.