AI 中文总结
针对配对重复测量数据中传统GBTM假设常被违反的问题，提出联合GBTM框架，开发两阶段方法和单阶段EM算法，经模拟验证可纠正偏差，还应用于实际数据研究听力表型及饮食与听力模式的关联。
AI 中文摘要
传统基于组的轨迹建模（GBTM）中的条件独立性假设常被具有异质轨迹模式的配对重复测量数据违反。随机效应模型虽能适应这种依赖性，但会夸大组内变异性并模糊不同表型形状。我们提出了一个联合GBTM框架，明确对配对轨迹中的层次依赖性进行建模，同时允许它们遵循不同的潜在模式。我们开发了一种稳健的两阶段方法来应对由罕见潜在组引起的估计挑战，以及一种单阶段EM算法作为平衡组大小情况下的理论基线。模拟表明我们的方法纠正了因忽略层次依赖性而导致的偏差。所提出的模型应用于护士健康研究II（NHS II）的一个子队列——听力保护研究（CHEARS）听力评估组（AAA）的实际数据，以识别不同的听力表型，并研究停止高血压饮食方法（DASH）饮食依从性评分与潜在听力模式之间的关联。
英文摘要
The assumption of conditional independence in conventional group-based trajectory modeling (GBTM) is often violated by paired repeated-measures data with heterogeneous trajectory patterns. While random-effects models can accommodate this dependence, they inflate within-group variability and blur distinct phenotypic shapes. We propose a joint GBTM framework that explicitly models hierarchical dependence in paired trajectories while allowing them to follow different latent patterns. We develop a robust two-stage approach to address estimation challenges caused by rare latent groups, and a one-stage EM algorithm that serves as a theoretical baseline under balanced group sizes. Simulations demonstrate that our methods correct the biases caused by ignoring hierarchical dependence. The proposed model was applied to real-world data from the Conservation of Hearing Study (CHEARS) Audiology Assessment Arm (AAA), a subcohort of the Nurses' Health Study II (NHS II), to identify distinct audiometric phenotypes and to investigate the association between the Dietary Approaches to Stop Hypertension (DASH) dietary adherence score and the latent audiometric patterns.
