leaspy: LEArning Spatiotemporal Patterns in PYthon

2026-08-10

AI Summary

This work addresses the challenge of temporal misalignment in longitudinal data arising from inter-individual differences in the onset and progression rates of dynamic processes. To this end, the authors introduce leaspy, an open-source Python library based on mixed-effects models. The framework enables multivariate modeling of continuous, time-to-event, and mixed data types within a unified formulation, facilitating both population-level trajectory estimation and individual-specific deviation capture. A dedicated time-warping algorithm is incorporated to align heterogeneous longitudinal observations across subjects. Notably, this is the first implementation to integrate multivariate heterogeneous longitudinal modeling in a scalable and robust software architecture. The method has been successfully applied in neurodegenerative disease research, where it effectively characterizes disease heterogeneity and yields accurate personalized predictions, demonstrating its practical utility and validity.

Abstract

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.

Problem

• temporal misalignment
• longitudinal data
• multivariate dynamics
• subject-specific variability
• disease progression

Innovation

• longitudinal modeling
• mixed effects model
• temporal misalignment
• multivariate dynamics
• open-source library