AI 中文总结
本研究提出针对混合尺度多元纵向结局的新型潜在轨迹模型，结合期望最大化算法，将其应用于ADNI研究，以识别动态亚组、揭示预后指标，支持个体化治疗规划。
AI 中文摘要
潜在轨迹分析是一种统计方法，它基于结局变量的相似性将患者划分为同质亚组，以此解释异质性。在临床工作中，患者往往不会遵循相同的病程或治疗反应，而传统分析通常对患者取平均值，掩盖了重要的亚组。本研究提出一种针对混合类型多元纵向结局的新型潜在轨迹模型，并采用期望最大化算法作为估计策略。该模型可识别个体水平、特定时间的潜在类别归属，以及描述潜在类别归属随时间变化的潜在轨迹归属。通过捕捉这些动态变化，我们可以突出显示预后不良风险较高的患者，揭示改善或衰退的早期指标，最终支持更个体化的治疗方案规划。我们将该方法应用于阿尔茨海默病神经影像倡议（ADNI），这是一项用于验证阿尔茨海默病（AD）临床试验生物标志物的多中心纵向观察性研究。
英文摘要
Latent trajectory analysis is a statistical method for explaining heterogeneity by partitioning patients into homogeneous subgroups based on similarities in outcome variables. In the context of clinical work, patients often do not follow the same course of illness or treatment response, and traditional analyses often average across patients, masking important subgroups. This work proposes a novel latent trajectory model for multivariate longitudinal outcomes with mixed types and uses the expectation-maximization algorithm as an estimation strategy. The proposed model can identify individual-level, time specific latent class memberships and a latent trajectory membership that describes how the latent class memberships change over time. By capturing these dynamic changes, we can highlight patients at higher risk of poor outcomes, reveal early indicators of improvement or decline, and ultimately support more individualized treatment planning. We present an application of our methodology to the Alzheimer's Disease Neuroimaging Initiative (ADNI), a longitudinal, multi-center, observational study to validate biomarkers for Alzheimer disease (AD) clinical trials.
