A dynamic prediction model for predicting the time at which patients with MCI progress to AD based on time-dependent covariates.

Background: Alzheimer's Disease (AD) is an irreversible neurodegenerative disorder that imposes a significant burden on families and society. Timely intervention during the transitional stages from Mild Cognitive Impairment (MCI) to AD can help mitigate this issue. The MCI-to-AD conversion time would be helpful if it could be predicted.

Developing novel dynamic prediction methods for survival time to analyze short-term and long-term progression of Alzheimer's disease.

Tracking and monitoring mild cognitive impairment (MCI) patients to intervene promptly at the imminent onset of Alzheimer's disease (AD) are crucial. However, existing dynamic survival prediction models for the conversion from MCI to AD are mostly based on hazard rates, which are less intuitive to interpret and require adherence to the proportional hazards assumption. To address this, we propose a Bayesian joint model (JM) based on the time scale indicator of the restricted mean survival time (RMST), which can capture the trajectories of multiple longitudinal covariates and dynamically predict the patient time to event.