03. Multi-state time-to-event modeling of Huntington Disease stage progression

Huntington's disease is a genetic neurodegenerative disorder characterized by progressive motor, cognitive, and behavioral impairments and caused by an expanded number of CAG repeats in the HTT gene. The Huntington's Disease Integrated Staging System (HD-ISS) classifies Huntington disease progression into four discrete stages using biological, clinical, and functional assessments, with stage criteria varying by age. Impairment due to Huntington disease is irreversible, so understanding and anticipating progression is critical to patient prognosis. While the HD-ISS captures patients' current disease stage, it does not compute the time to the next stage, information crucial for clinical management, patient knowledge, and future planning. To address this clinical need, we developed a statistical model to estimate the time to progression between HD-ISS stages using the PREDICT-HD study data. We employ a multi-state framework of accelerated failure time survival regression models with various distributions to analyze time-to-stage transition data. Across all stages, we incorporate genetic information and biological sex as covariates and estimate the mean time patients spend in each Huntington's disease stage. To select the final model, performance is evaluated using the area under the receiver operating characteristic curve to assess discrimination between those who will and will not transition from one stage to the next. Our selected model provides individualized estimates of stage progression timing to support clinical decision-making and patient care.

multi-state modeling

neurodegenerative disease

censoring

survival analysis

accelerated failure-time model

disease progression 

Mid-Level

Knowledge

Women in Statistics and Data Science 2025