A Nonparametric Approach for Estimating the Effective Sample Size in Gaussian Approximation of Expected Value of Sample Information

TL;DR

This paper introduces a new nonparametric method to efficiently estimate the effective sample size (ESS) for Gaussian approximations of EVSI, improving accuracy and reducing computational costs.

Contribution

The paper presents a novel nonparametric approach to estimate ESS using summary statistics and regression, addressing computational and accuracy limitations of existing methods.

Findings

The proposed method accurately estimates ESS with low computational cost.

Simulation results demonstrate improved efficiency over traditional methods.

The approach enhances understanding of uncertainty in complex probability distributions.

Abstract

The effective sample size (ESS) measures the informational value of a probability distribution in terms of an equivalent number of study participants. The ESS plays a crucial role in estimating the Expected Value of Sample Information (EVSI) through the Gaussian approximation approach. Despite the significance of ESS, existing ESS estimation methods within the Gaussian approximation framework are either computationally expensive or potentially inaccurate. To address these limitations, we propose a novel approach that estimates the ESS using the summary statistics of generated datasets and nonparametric regression methods. The simulation results suggest that the proposed method provides accurate ESS estimates at a low computational cost, making it an efficient and practical way to quantify the information contained in the probability distribution of a parameter. Overall, determining the ESS can help analysts understand the uncertainty levels in complex prior distributions in the probability analyses of decision models and perform efficient EVSI calculations.