AI 中文总结
本研究针对全国女性健康研究（SWAN）的长期随访数据，提出联合多层潜在转移模型，将潜在类别转移序列聚类并关联自我报告跌倒结局，还通过模拟评估了方法的运行特性。
AI 中文摘要
全国女性健康研究（SWAN）已对女性进行了超过30年的随访，从中年绝经前阶段直至晚年。该研究包含16次调查，间隔约2年，涵盖广泛的身心症状。这些多元分类调查反应可能蕴含丰富的健康相关信息，其时间轨迹可通过反应剖面及随时间演变的反应动态加以表征。为捕捉这两个特征并探究其对后续健康结局的指示作用，我们提出了一种联合多层潜在转移模型。该模型将基于个体随时间反应剖面进行分类的潜在转移模型，与对这些潜在类别转移序列的额外聚类层相结合，旨在将这些聚类剖面与健康结局（本研究中为自我报告的跌倒）关联起来。此外，我们通过模拟研究评估了该方法的运行特性。
英文摘要
The Study of Women's Health Across the Nation (SWAN) has followed women for over 30 years, from midlife premenopause until later life. The study has 16 surveys at approximately 2 years intervals that cover a wide range of physical and psychological symptoms. These multivariate categorical survey responses potentially contain rich health-related information. Temporal trajectories of the survey responses can be characterized by both the responses profiles and the evolving dynamics of the responses over time. To capture those two features and investigate how they inform subsequent health outcomes, we propose a joint multi-layer latent transition model. We combine a latent transition model that classifies individuals based on their response profiles over time with an additional layer of clustering of these latent class transition sequences, with the goal of connecting these cluster profiles with health outcomes: in this application, self-reported falls. In addition, we evaluate the operating characteristics of the method through simulation studies.
