Reflections on dynamic prediction of Alzheimer's disease: advancements in modeling longitudinal outcomes and time-to-event data.

Abstract

Background: Individualized prediction of health outcomes supports clinical medicine and decision making. Our primary objective was to offer a comprehensive survey of methods for the dynamic prediction of Alzheimer's disease (AD), encompassing both conventional statistical methods and deep learning techniques.

Methods: Articles were sourced from PubMed, Embase and Web of Science databases using keywords related to dynamic prediction of AD. A set of criteria was developed to identify included studies. The correlation information for the construction of models was extracted.

Results: We identified four methodological frameworks for dynamic prediction from 18 studies with two-stage model (n = 3), joint model (n = 11), landmark model (n = 2) and deep learning (n = 2). We reported and summarized the specific construction of models and their applications.

Conclusions: Each framework possesses distinctive principles and attendant benefits. The dynamic prediction models excel in predicting the prognosis of individual patients in a real-time manner, surpassing the limitations of traditional baseline-only prediction models. Future work should consider various data types, complex longitudinal data, missing data, assumption violations, survival outcomes, and interpretability of models.