The progression of chronic diseases often follows highly variable trajectories, and the underlying factors remain poorly understood. Standard mixed-effects models typically represent inter-patient differences as random deviations around a common reference, which may obscure meaningful subgroups. We propose a probabilistic mixture extension of a mixed effects model, the Disease Course Mapping model, to identify distinct disease progression subtypes within a population. The mixture structure is introduced at the latent individual parameters, enabling clustering based on both temporal and spatial variability in disease trajectories. We evaluated the model through simulation studies to assess classification performance and parameter recovery. Classification accuracy exceeded 90% in simpler scenarios and remained above 80% in the most complex case, with particularly high recall and precision for fast-progressing clusters. Compared to a post hoc classification approach, the proposed model yielded more accurate parameter estimates, smaller biases, lower root mean squared errors, and reduced uncertainty. It also correctly recovered the true three-cluster structure in 93% of the simulations. Finally, we applied the model to a longitudinal cohort of CADASIL patients, identifying two clinically meaningful clusters, differentiating patients with early versus late onset and fast versus slow progression, with clear spatial patterns across motor and memory scores. Overall, this probabilistic mixture framework offers a robust, interpretable approach for clustering patients based on spatiotemporal disease dynamics.

翻译：慢性疾病的进展通常呈现高度异质性的轨迹，其潜在机制仍不甚明晰。标准混合效应模型通常将患者间差异表示为共同参考轨迹周围的随机偏差，这可能掩盖有意义的亚群。我们提出一种混合效应模型的概率混合扩展——疾病进程映射模型，用于识别人群内不同的疾病进展亚型。该混合结构被引入潜在个体参数层面，从而能够基于疾病轨迹的时间和空间变异性进行聚类。我们通过模拟研究评估了模型的分类性能与参数恢复能力。在简单场景中分类准确率超过90%，在最复杂情况下仍保持在80%以上，其中快速进展类群的召回率与精确度尤为突出。与事后分类方法相比，所提模型能获得更准确的参数估计、更小的偏差、更低的均方根误差及更小的不确定性。在93%的模拟中该模型正确恢复了真实的三聚类结构。最后，我们将模型应用于CADASIL患者的纵向队列，识别出两个具有临床意义的聚类：早发与晚发、快速进展与缓慢进展的患者群体，其运动与记忆评分呈现出清晰的空间分布模式。总体而言，这一概率混合框架为基于疾病时空动态特征的患者聚类提供了稳健且可解释的研究方法。