神经退行性疾病的特征是存在众多进展标记物与临床终点。例如，多系统萎缩（Multiple System Atrophy, MSA）是一种罕见的神经退行性突触核蛋白病，其特征为进行性自主神经功能衰竭与运动功能障碍的多种组合，且预后极差。描述这类复杂且多维度疾病的进展尤为困难，研究人员必须同时考量多变量标记物随时间的评估结果、临床终点的发生情况，以及患者间高度可疑的异质性。然而，这类描述对于理解疾病的自然史、对确诊患者进行分期、揭示亚表型以及预测预后至关重要。\n通过MSA进展的案例，我们展示了一种对多个重复测量标记物与临床终点建模的潜在类别方法如何帮助描述复杂疾病进展，并识别用于探索新病理假说的亚表型。所提出的联合潜在类别模型包含两类子模型：一类是类别特异性多变量混合模型，用于处理可能被归纳为潜在维度的多变量重复生物标记物；另一类是类别与原因特异性比例风险模型，用于处理事件时间数据。经模拟验证的最大似然估计程序已在lcmm R包中提供。\n在纳入598名患者、随访时长最长达13年的法国MSA队列中，研究识别出5种MSA亚表型，它们在生物标记物退化的顺序与形态、以及相关的死亡风险上存在差异。在后验分析中，这5种亚表型被用于探索临床进展与外部影像学及体液生物标记物之间的关联，同时适当考量了亚表型归属的不确定性。
英文摘要：
Neurodegenerative diseases are characterized by numerous markers of progression and clinical endpoints. For instance, Multiple System Atrophy (MSA), a rare neurodegenerative synucleinopathy, is characterized by various combinations of progressive autonomic failure and motor dysfunction, and a very poor prognosis. Describing the progression of such complex and multi-dimensional diseases is particularly difficult. One has to simultaneously account for the assessment of multivariate markers over time, the occurrence of clinical endpoints, and a highly suspected heterogeneity between patients. Yet, such description is crucial for understanding the natural history of the disease, staging patients diagnosed with the disease, unravelling subphenotypes, and predicting the prognosis. Through the example of MSA progression, we show how a latent class approach modeling multiple repeated markers and clinical endpoints can help describe complex disease progression and identify subphenotypes for exploring new pathological hypotheses. The proposed joint latent class model includes class-specific multivariate mixed models to handle multivariate repeated biomarkers possibly summarized into latent dimensions and class-and-cause-specific proportional hazard models to handle time-to-event data. Maximum likelihood estimation procedure, validated through simulations is available in the lcmm R package. In the French MSA cohort comprising data of 598 patients during up to 13 years, five subphenotypes of MSA were identified that differ by the sequence and shape of biomarkers degradation, and the associated risk of death. In posterior analyses, the five subphenotypes were used to explore the association between clinical progression and external imaging and fluid biomarkers, while properly accounting for the uncertainty in the subphenotypes membership.
A joint model for (un)bounded longitudinal markers, competing risks, and recurrent events using patient registry data
Erasmus University Medical Center（伊拉斯姆斯大学医学中心）；Duke University（杜克大学）；Cincinnati Children’s Hospital Medical Center（辛辛那提儿童医院医疗中心）；University of Cincinnati（辛辛那提大学）
Toward Joint Prediction of a Longitudinal Marker and a Terminal Event: A bivariate discrete-time framework
Harvard T.H. Chan School of Public Health（哈佛陈曾熙公共卫生学院）；Richard A. and Susan F. Smith Center for Outcomes Research, Beth Israel Deaconess Medical Center（贝斯以色列女执事医疗中心理查德·A和苏珊·F史密斯结果研究中心）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）
Disease Progression Modelling and Stratification for detecting sub-trajectories in the natural history of pathologies: application to Parkinson's Disease trajectory modelling
Decoupling risk and masking in mammographic density under irregular follow up using a latent Markov progression detection framework
University of Toronto（多伦多大学）；Vector Institute for Artificial Intelligence（向量人工智能研究所）；Qatar University（卡塔尔大学）；Middle East Technical University（中东理工大学）；Etlik City Hospital Oncology（埃特利克城市医院肿瘤科）
