Work

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

Year: 2025

Type: article

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.

Source: Stats

DOI: 10.3390/stats8030074

Authors: Franklin Fernando Massa, Marco Scavino, Graciela Muniz-Terrera

Institutions: Universidad de la República de Uruguay, Ohio University

Language: English

Topic: Statistical Methods and Bayesian Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $1,930