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.

翻译：有效样本量（ESS）通过等效研究参与者数量衡量概率分布的信息价值。在高斯近似框架下估算样本信息期望值（EVSI）时，ESS发挥着关键作用。尽管ESS至关重要，但目前高斯近似框架内的ESS估计方法要么计算成本高昂，要么可能产生不准确的结果。针对这些局限，我们提出了一种新方法，该方法利用生成数据集的汇总统计量与核非参数回归来估计ESS。模拟结果表明，所提方法能以较低计算成本提供准确的ESS估计值，从而为量化参数概率分布所包含的信息提供了高效实用的途径。总体而言，确定ESS有助于分析者理解决策模型概率分析中复杂先验分布的不确定性水平，并实现高效的EVSI计算。