Estimating the Expected Value of Sample Information across Different Sample Sizes Using Moment Matching and Nonlinear...

Area of Science:

• Health economics
• Decision analysis
• Biostatistics

Background:

• The expected value of sample information (EVSI) quantifies the economic value of future studies for health economic models.
• EVSI can guide trial design by comparing the cost and value of different designs to maximize net benefit.
• Current EVSI analysis can be computationally intensive, particularly when optimizing numerous trial designs.

Purpose of the Study:

• To develop a computationally efficient method for calculating EVSI across different sample sizes.
• To enable the optimization of trial designs based on their expected economic value.

Main Methods:

• Extends the moment-matching approach for EVSI estimation.
• Optimizes for various trial sample sizes with comparable computational cost to single estimates.
• Estimates EVSI across sample sizes using Bayesian nonlinear regression on posterior variance of net monetary benefit.

Main Results:

• Demonstrates a fast and accurate EVSI calculation method using a chronic pain intervention model.
• Highlights the utility of EVSI for comparing different trial designs.
• Confirms the method's applicability to realistic health economic models.

Conclusions:

• The proposed method provides a fast and accurate way to calculate EVSI across sample sizes.
• Enables researchers to identify economically optimal trial designs for reducing uncertainty in health economic models.