发表机构
MRC Biostatistics Unit, University of Cambridge（剑桥大学医学研究委员会生物统计学科）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文综述了微观模拟中模拟多种疾病风险因素的统计方法，比较其适用性，并通过心血管健康检查案例展示实践，为卫生政策建模提供方法支持。
AI 中文摘要
用于评估慢性病风险降低政策和情景的纵向微观模拟模型，通常涉及随时间对合成人群的多种风险因素进行模拟。已有多种统计方法用于实现这一目标，但这些方法背后的原理从未被全面综述或批判性比较。我们描述并综述了在给定纵向或重复横截面数据的情况下，用于随时间模拟多种风险因素的一系列方法，重点指出其中最具实际适用性和灵活性的方法，并介绍了一些有用的变体。我们描述了在实践中基于数据拟合度来检查和比较这些方法的程序，并展示了一个案例研究，其中基于评估中年心血管健康检查的微观模拟模型，对这些方法进行了使用和比较。我们得出结论，现有一系列有用的程序可供使用，本文提升了支持其在为卫生政策提供信息的模型中使用的知识基础。
英文摘要
Longitudinal microsimulation models to evaluate policies and scenarios for chronic disease risk reduction typically involve simulating multiple risk factors for a synthetic population over time. Various statistical methods have been used to accomplish this, but the principles behind them have never been comprehensively reviewed or critically compared. We describe and review the range of methods that have been used to simulate multiple risk factors over time, given either longitudinal or repeated cross-sectional data, highlight those which are most practically applicable and flexible, and introduce some useful variants of them. We describe procedures that can be used to check and compare these methods in practice based on fit to data, and present a case study where the methods are used and compared, based on a microsimulation model to evaluate mid-life cardiovascular health checks. We conclude that a useful range of procedures are available, and this paper improves the knowledge base to support their use in models to inform health policy.
