Landmark Multi-State Modeling for Complex Disease Trajectories

G. Gómez Melis, I. Arostegui Madariaga, K. Langohr

Analyzing complex disease trajectories using multi-state models (MSMs) requires handling both complex event structures and the computational demands of large-scale registries. We introduce an MSM framework extending beyond simple illness-death structures to model time-to-recurrence (relapse or reinfection) by integrating data from initial events.
We address two challenges: defining recurrence at fixed post-first infection intervals via landmark constraints, and managing a population-level sample of 400,000 individuals, a scale exceeding conventional tools like MSMpred. Applied to COVID-19 data in the Basque Country, our methodology utilizes Cox cause-specific hazard models. While the initial model uses population registries, the second is restricted to individuals at risk after a specific threshold. We propose and compare several approaches incorporating baseline and time-dependent covariates at the landmark time. This framework is adaptable to cancer and other infectious diseases.

Keywords: Disease recurrence,Landmark analysis, arge-scale registries, Multi-state models, Survival Analysis,

GT Bioestadística SEIO - BIOSTATNET: Análisis de Supervivencia
September 2, 2026  3:30 PM
Aula B

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