Precise Characterization of the Longitudinal Cognitive Decline Toward Alzheimer's Disease Progression: A Novel Double Anchoring Events-Based Sigmoidal Mixed Model

Thomas M. Menino Convention & Exhibition Center 

Accurately characterizing cognitive decline across multiple domains on a clinically meaningful time scale is critical for understanding Alzheimer's disease (AD) progression, yet difficult to achieve because the disease process spans decades. Consequently, most available datasets consist of fragmentary individual data, which captures only random segments of an individual's potential progression. This can lead to conflicting conclusions when analyzed using standard strategies. To overcome these data challenges, we proposed a novel statistical framework, the double anchoring events-based sigmoidal mixed model (DSMM), and applied it to harmonized data from 13 cohorts within the ADSP-PHC. The DSMM characterizes cognitive decline trajectories relative to time-to-incident AD onset; uniquely, it introduces the concept of "secondary anchoring events" to effectively align trajectories for participants without an observed incident AD diagnosis. This innovation integrates fragmentary data into a cohesive model, significantly reducing selection bias while ensuring the replicability, interpretability, and representativeness of the resulting trajectories. Our results revealed a robust temporal order where memory decline generally precedes language impairment, followed by executive function. This pattern persists across subgroups defined by APOE ε4 status, sex, and race/ethnicity, highlighting how advanced statistical modeling of harmonized multi-cohort data can successfully resolve discrepancies in disease progression research.