Department of Health Policy and Management, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA (HJ).
Department of Health Policy and Management, School of Public Health, University of Minnesota, Minneapolis, MN, USA (FA-E).
Med Decis Making. 2018 Feb;38(2):174-188. doi: 10.1177/0272989X17715627. Epub 2017 Jul 22.
Most decisions are associated with uncertainty. Value of information (VOI) analysis quantifies the opportunity loss associated with choosing a suboptimal intervention based on current imperfect information. VOI can inform the value of collecting additional information, resource allocation, research prioritization, and future research designs. However, in practice, VOI remains underused due to many conceptual and computational challenges associated with its application. Expected value of sample information (EVSI) is rooted in Bayesian statistical decision theory and measures the value of information from a finite sample. The past few years have witnessed a dramatic growth in computationally efficient methods to calculate EVSI, including metamodeling. However, little research has been done to simplify the experimental data collection step inherent to all EVSI computations, especially for correlated model parameters. This article proposes a general Gaussian approximation (GA) of the traditional Bayesian updating approach based on the original work by Raiffa and Schlaifer to compute EVSI. The proposed approach uses a single probabilistic sensitivity analysis (PSA) data set and involves 2 steps: 1) a linear metamodel step to compute the EVSI on the preposterior distributions and 2) a GA step to compute the preposterior distribution of the parameters of interest. The proposed approach is efficient and can be applied for a wide range of data collection designs involving multiple non-Gaussian parameters and unbalanced study designs. Our approach is particularly useful when the parameters of an economic evaluation are correlated or interact.
大多数决策都伴随着不确定性。信息价值（VOI）分析量化了基于当前不完善信息选择次优干预措施所带来的机会损失。VOI 可以为收集额外信息、资源分配、研究优先级排序和未来研究设计提供信息。然而，在实践中，由于其应用所涉及的许多概念和计算挑战，VOI 的应用仍然不足。预期样本信息价值（EVSI）植根于贝叶斯统计决策理论，衡量从有限样本中获取信息的价值。过去几年，计算有效方法（包括建模）在计算 EVSI 方面取得了显著进展。然而，对于简化所有 EVSI 计算中固有的实验数据收集步骤，特别是对于相关模型参数，研究甚少。本文基于 Raiffa 和 Schlaifer 的原始工作，提出了一种计算 EVSI 的传统贝叶斯更新方法的通用高斯逼近（GA）。该方法使用单个概率敏感性分析（PSA）数据集，涉及 2 个步骤：1）线性建模步骤，在后验分布上计算 EVSI；2）GA 步骤，计算感兴趣参数的后验分布。该方法效率高，可应用于涉及多个非高斯参数和不平衡研究设计的广泛数据收集设计。当经济评估的参数相关或相互作用时，我们的方法特别有用。
