老年痴呆是严重危害老年人生命健康并造成极大负担的重大公共卫生问题，个体化痴呆风险动态预测有助于高危人群早期筛查，是精准医学和有限医疗卫生资源最优化分配的依据。项目组前期基于社区老年人群认知流行病学队列研究，采用多状态模型探讨了传统危险因素在痴呆自然史不同阶段的不同作用以及基于危险因素的转归预测。考虑到具有潜变量特质的认知功能只能由多个神经心理学测试量表在不同时间点重复测量间接观察，构成纵向过程；认知与终点事件（痴呆/死亡）有关，构成生存过程；同时老年人群是一个具有强异质性的总体，协变量不能完全解释该异质性。本项目构建基于潜在结构的纵向过程和生存过程的联合模型，通过潜在类别进一步解释总体异质性，计算基于认知功能多维测量的个体痴呆风险动态预测概率，并进行不同建模策略预测效能比较。研究结果可为识别认知干预重点人群提供统计决策支持，并为其他具有潜在特质属性的纵向标记物和多终点事件统计建模提供参考。

As a major public health issue, dementia will not only endanger health of the elderly, but put burdens heavily on patients, caregivers, societies and health care systems as well. Personalized cognitive degradation risk dynamic prediction models may enable early detection of the subjects that are subject to risk of cognitive decline so as to lay the theoretical foundation for precision medicine and optimally allocating the limited health-care resources. Based on the cognition epidemiological cohort study among the community-dwelling elderly people, we built multi-state models to determine the different effects of known risk factors in the cognitive impairment process and to make prediction. The following issues should be considered simultaneously. Cognition which is the central longitudinal process in dementia natural history is not directly observed but measured by multiple psychometric tests collected repeatedly at cohort visits, which is longitudinal process. In addition, cognitive decline is very associated with onset of dementia or death, which is survival process. A further issue that should be accounted for is the strong unobservable heterogeneity between subjects that may not be explained by the covariates. A population of elderly subjects usually mixes groups of subjects with different types of cognitive trajectory (i.e. latent classes). This study aims to build a joint latent class mixed model for jointly analyzing longitudinal process and survival process to further explain heterogeneity between subjects by latent classes, to calculate individual dementia risk dynamic prediction probabilities based on cognitive measures, and to evaluate the predictive abilities for different model built strategies. This project will provide statistical support for health-care providers to identify subjects who may have unrecognized cognitive impairment or undiagnosed dementia among the elders. It will also provide model built references for multiple longitudinal markers with latent trait character and multiple terminal events.