  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.
