具有区间截断事件时间的变更点的多变量纵向和事件时间数据联合分析
Joint analysis for multivariate longitudinal and event time data with a change point anchored at interval-censored event time
AI总结：
本文提出了一种联合模型，用于分析具有区间截断事件时间的多变量纵向和事件时间数据，以探讨亨廷顿病中认知障碍与运动功能障碍的相互作用。
摘要：
亨廷顿病（HD）是一种常染色体显性神经退行性 disorder，其特征是运动功能障碍、精神障碍和认知下降。HD的发病标志是严重的运动障碍，这可能通过先前的认知下降预测，并反过来加剧认知缺陷。然而，临床数据通常是在离散时间点收集的，因此疾病发病时间会受到区间截断的影响。为了解决此类数据带来的挑战，我们开发了一个联合模型，用于多变量纵向生物标志物，其中变更点锚定在区间截断的事件时间上。该模型同时评估了纵向生物标志物对事件时间的影响以及事件后生物标志物轨迹的变化。我们进行了全面的模拟研究，以展示所提出方法在因果推断中的有限样本性能。最后，我们将该方法应用于PREDICT-HD，一个多中心观察性队列研究，以确定认知障碍和运动功能障碍在疾病进展中的相互作用。
英文摘要：
Huntington's disease (HD) is an autosomal dominant neurodegenerative disorder characterized by motor dysfunction, psychiatric disturbances, and cognitive decline. The onset of HD is marked by severe motor impairment, which may be predicted by prior cognitive decline and, in turn, exacerbate cognitive deficits. Clinical data, however, are often collected at discrete time points, so the timing of disease onset is subject to interval censoring. To address the challenges posed by such data, we develop a joint model for multivariate longitudinal biomarkers with a change point anchored at an interval-censored event time. The model simultaneously assesses the effects of longitudinal biomarkers on the event time and the changes in biomarker trajectories following the event. We conduct a comprehensive simulation study to demonstrate the finite-sample performance of the proposed method for causal inference. Finally, we apply the method to PREDICT-HD, a multisite observational cohort study of prodromal HD individuals, to ascertain how cognitive impairment and motor dysfunction interact during disease progression.
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