A Bayesian Non-Linear Mixed-Effects Model for Accurate Detection of the Onset of Cognitive Decline in Longitudinal Aging Studies

Franklin Fernando Massa (), Marco Scavino and Graciela Muniz-Terrera
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Franklin Fernando Massa: Departamento de Métodos Cuantitativos, Universidad de la República, Montevideo 11400, Uruguay
Marco Scavino: Departamento de Métodos Cuantitativos, Universidad de la República, Montevideo 11400, Uruguay
Graciela Muniz-Terrera: Heritage College Osteopathic Medicine, Ohio University, Athens, OH 45701, USA

Stats, 2025, vol. 8, issue 3, 1-16

Abstract: Change-point models are frequently considered when modeling phenomena where a regime shift occurs at an unknown time. In aging research, these models are commonly adopted to estimate of the onset of cognitive decline. Yet these models present several limitations. Here, we present a Bayesian non-linear mixed-effects model based on a differential equation designed for longitudinal studies to overcome some limitations of classical change point models used in aging research. We demonstrate the ability of the proposed model to avoid biases in estimates of the onset of cognitive impairment in a simulated study. Finally, the methodology presented in this work is illustrated by analyzing results from memory tests from older adults who participated in the English Longitudinal Study of Aging.

Keywords: change point models; cognitive decline; non-linear mixed effects models; Bayesian modelling (search for similar items in EconPapers)
JEL-codes: C1 C10 C11 C14 C15 C16 (search for similar items in EconPapers)
Date: 2025
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