Multi-state Models For Modeling Disease Histories Based On Longitudinal Data

2025-09-24

AI Summary

This study addresses multiple methodological challenges in modeling multistage diseases (e.g., chronic kidney disease) using longitudinal data—namely, competing risks, dependent left truncation, multiple time scales, index-event bias, and interval censoring—by proposing an extended piecewise exponential additive model (PAM). Methodologically, the framework integrates a competing-risks formulation, interval-censoring correction, and flexible covariate modeling across distinct time scales, while systematically quantifying index-event bias and its dependence on the completeness of covariate adjustment. Applied to UK Biobank data, the model yields accurate estimates of disease incidence and progression risks: it reveals higher progression risk among early-onset patients, with risk increasing monotonically with age; and identifies rs77924615 at the UMOD locus as significantly associated only with disease onset—not with subsequent progression. This work provides a statistically rigorous yet computationally feasible framework for modeling complex disease trajectories in observational longitudinal studies.

Technology Category

• Reasoning under Uncertainty: Relational Probabilistic Models
• Data Mining & Knowledge Management: Mining of Visual, Multimedia & Multimodal Data
• Knowledge Representation and Reasoning: Nonmonotonic Reasoning

Application Category

• User Modeling, Personalization and Recommendation: Studies of user behavior, including longitudinal effects of personalized systems
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution

Abstract

Multi-stage disease histories derived from longitudinal data are becoming increasingly available as registry data and biobanks expand. Multi-state models are suitable to investigate transitions between different disease stages in presence of competing risks. In this context, however their estimation is complicated by dependent left-truncation, multiple time scales, index event bias, and interval-censoring. In this work, we investigate the extension of piecewise exponential additive models (PAMs) to this setting and their applicability given the above challenges. In simulation studies we show that PAMs can handle dependent left-truncation and accommodate multiple time scales. Compared to a stratified single time scale model, a multiple time scales model is found to be less robust to the data generating process. We also quantify the extent of index event bias in multiple settings, demonstrating its dependence on the completeness of covariate adjustment. In general, PAMs recover baseline and fixed effects well in most settings, except for baseline hazards in interval-censored data. Finally, we apply our framework to estimate multi-state transition hazards and probabilities of chronic kidney disease (CKD) onset and progression in a UK Biobank dataset (n=$142,667$). We observe CKD progression risk to be highest for individuals with early CKD onset and to further increase over age. In addition, the well-known genetic variant rs77924615 in the UMOD locus is found to be associated with CKD onset hazards, but not with risk of further CKD progression.

Problem

• Modeling disease progression transitions with competing risks
• Handling statistical challenges like left-truncation and interval-censoring
• Estimating chronic kidney disease onset and progression probabilities

Innovation

• Extends piecewise exponential additive models for disease histories
• Handles dependent left-truncation and multiple time scales
• Quantifies index event bias in multi-state transition modeling