脑卒中严重负担状态已由城市转向农村，因缺乏其适宜风险预警工具而难以尽早干预。Framingham模型为代表的预测工具对农村人群缺乏适宜性且其设计构思、指标筛选、风险计算、建模方法均有局限性。本项目提出风险预警适宜性原则和贝叶斯竞争风险建模策略。依此，针对不同设计（多中心队列、全人群病例对照、病例随访队列）进行创新研究：构思上，将脑卒中自然史划为发生和转归两阶段分别建模；指标上，用适宜性分级策略遴选风险预警因子；风险计算上，将绝对风险、终身风险和相对绝对风险相结合，全面评估个体和群体风险状态；建模上，基于贝叶斯多水平竞争风险模型，利用贝叶斯模型选择，通过调整模型参数先验和建立备选模型集以适宜于不同目标人群；实践上，将复杂模型开发为简易量表工具及软件包。预期目标是以脑卒中为例建立慢病风险预警新方法，构建农村人群脑卒中发生和转归的适宜预警模型、量表工具及软件包；为慢性病风险评估提供新策略新方法。

The heavy burdens of stroke were seen shifting from urban to rural areas in China. Without an assessment tool and method for accuraterisk prediction, effective early detection and intervention becomes infeasible. Because Framingham modelwas designed many years ago in the United States, its limitation in study design, variable selection, and risk calculation makes it not suitable forpredicting risks of stroke and other cardiovasculardiseases among rural populations of China. In this project, following the principal of suitability and adaptability, we propose to develop a new strategy of risk prediction using Bayesian hierarchical competing-risk modeling approach. This innovative strategy is able to accommodate data from multiple sources with different epidemiologic study designs (i.e., a multi-center cohort, a case-control study, and a stroke registry). By dividing lifespan into pre-stroke and post-stroke phases, within the framework of Bayesian hierarchical modeling with random-effects indescribing the heterogeneity between sub-populations, we willdevelop risk-competing models for predicting the risks of different types of stroke and those of their prognostic outcomes. The method of Bayesian variable selection will be used in selecting significant predictors from a list of risk factors that are pre-categorized into sub-groups according to their cost and convenience in acquisition and clinical relevance. By developing a set of candidate models, each with distinctive combination of risk factors and baseline risk function estimates, the approach of Bayesian Model Averaging will be applied to calculate averaged absolute risks, lifespan risks, and relative absolute risksto jointly describe risks of a target individual or community. By adopting this strategy, we aim to study the feasibility of developing the next-generation flexible, adaptable and suitable risk assessment methods and computing tools (instruments and computer software packages) to specifically serve varying needs of different rural populations in China. Accomplishment of this project will offer evidence in developing new methods and tools for effectively and adaptively predicting risks of not only stroke but other chronic diseases in rural area of China.