AI 中文总结
leaspy是基于混合效应模型的开源Python库，可处理纵向数据的时间错位与多变量动态问题，支持多种数据类型，已应用于神经退行性疾病进展建模，为纵向进展建模提供健壮易用工具。
AI 中文摘要
纵向数据是各学科建模复杂系统随时间演化的基础，这类场景的核心挑战是处理时间错位：不同研究对象经历相似的基础过程，但速度和起始时间各不相同。当追踪多变量动态时，特征间存在动态交互而非遵循简单独立路径，这一困难会进一步加剧。为应对这些挑战，我们提出leaspy（Python中时空模式的学习工具库，LEArning Spatiotemporal patterns in PYthon），这是一个开源Python库。leaspy基于混合效应模型构建，支持估计群体水平轨迹，同时考虑研究对象的特异性变异；支持连续型、时间-事件（联合）、混合模型等多种数据类型的多变量形式，已成功应用于表征疾病异质性、生成个体预测。我们通过在神经退行性疾病进展中的应用验证其实际效用。该库遵循现代软件工程实践开发，包括系统测试和持续集成，便于集成新模型，为纵向进展建模提供了健壮且用户友好的工具库。
英文摘要
Longitudinal data are fundamental across scientific disciplines for modeling how complex systems evolve over time. A core challenge in these settings is handling temporal misalignment: different subjects undergo a similar underlying process but at varying speeds and starting times. This difficulty is further compounded when tracking multivariate dynamics, where features interact dynamically rather than following simple, independent pathways. To address these challenges, we present leaspy (LEArning Spatiotemporal patterns in PYthon), an open-source Python library. Built on a mixed effects model, leaspy enables the estimation of population-level trajectories while accounting for subject-specific variability. The library supports multivariate formulation across diverse data types, including continuous, time-to-event (joint), and mixture models-and has been successfully applied to characterize disease heterogeneity, and generate individual predictions We demonstrate its practical utility through an application in neurodegenerative disease progression. Developed following modern software engineering practices, including systematic testing and continuous integration, leaspy facilitates the integration of new models and provides a robust user-friendly library for longitudinal progression modeling.
