Jackson Christopher, Johnson Robert, de Nazelle Audrey, Goel Rahul, de Sá Thiago Hérick, Tainio Marko, Woodcock James
MRC Biostatistics Unit, University of Cambridge, Cambridge, UK.
MRC Biostatistics Unit, University of Cambridge, Cambridge, UK; and Imperial College London, London, UK.
Epidemiol Methods. 2021 Nov 15;10(1):20210012. doi: 10.1515/em-2021-0012. eCollection 2021.
Health impact simulation models are used to predict how a proposed policy or scenario will affect population health outcomes. These models represent the typically-complex systems that describe how the scenarios affect exposures to risk factors for disease or injury (e.g. air pollution or physical inactivity), and how these risk factors are related to measures of population health (e.g. expected survival). These models are informed by multiple sources of data, and are subject to multiple sources of uncertainty. We want to describe which sources of uncertainty contribute most to uncertainty about the estimate or decision arising from the model. Furthermore, we want to decide where further research should be focused to obtain further data to reduce this uncertainty, and what form that research might take. This article presents a tutorial in the use of Value of Information methods for uncertainty analysis and research prioritisation in health impact simulation models. These methods are based on Bayesian decision-theoretic principles, and quantify the expected benefits from further information of different kinds. The about a parameter measures sensitivity of a decision or estimate to uncertainty about that parameter. The represents the expected benefit from a specific proposed study to get better information about the parameter. The methods are applicable both to situationswhere the model is used to make a decision between alternative policies, and situations where the model is simply used to estimate a quantity (such as expected gains in survival under a scenario). This paper explains how to calculate and interpret the expected value of information in the context of a simple model describing the health impacts of air pollution from motorised transport. We provide a general-purpose R package and full code to reproduce the example analyses.
健康影响模拟模型用于预测拟议的政策或情景将如何影响人群健康结果。这些模型代表了通常复杂的系统，描述了情景如何影响疾病或伤害风险因素的暴露（例如空气污染或缺乏身体活动），以及这些风险因素如何与人群健康指标（例如预期生存率）相关。这些模型以多种数据来源为依据，并受到多种不确定性来源的影响。我们希望描述哪些不确定性来源对模型产生的估计或决策的不确定性贡献最大。此外，我们希望确定应将进一步研究重点放在何处，以获取更多数据来降低这种不确定性，以及该研究可能采取何种形式。本文介绍了在健康影响模拟模型中使用信息价值方法进行不确定性分析和研究优先级排序的教程。这些方法基于贝叶斯决策理论原则，并量化了不同类型进一步信息的预期收益。关于一个参数的[此处原文缺失相关术语]衡量决策或估计对该参数不确定性的敏感性。[此处原文缺失相关术语]代表特定拟议研究获取有关该参数更好信息的预期收益。这些方法既适用于模型用于在替代政策之间做出决策的情况，也适用于模型仅用于估计数量（例如情景下预期生存收益）的情况。本文解释了如何在描述机动交通空气污染对健康影响的简单模型背景下计算和解释信息的预期价值。我们提供了一个通用的R包和完整代码以重现示例分析。
