Accurate EVSI Estimation for Nonlinear Models Using the Gaussian Approximation Method.

ABSTRACT:

Background

The expected value of sample information (EVSI) measures the expected benefits that could be obtained by collecting additional data. Estimating EVSI using the traditional nested Monte Carlo method is computationally expensive, but the recently developed Gaussian approximation (GA) approach can efficiently estimate EVSI across different sample sizes. However, the conventional GA may result in biased EVSI estimates if the decision models are highly nonlinear. This bias may lead to suboptimal study designs when GA is used to optimize the value of different studies. Therefore, we extend the conventional GA approach to improve its performance for nonlinear decision models.

Methods

Our method provides accurate EVSI estimates by approximating the conditional expectation

SUBMITTER: Li L 

PROVIDER: S-EPMC11492544 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature