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

TL;DR

This paper introduces a Bayesian non-linear mixed-effects model based on differential equations to accurately detect the onset of cognitive decline in longitudinal aging studies, addressing limitations of traditional change-point models.

Contribution

The paper presents a novel Bayesian non-linear mixed-effects model that improves estimation accuracy of cognitive decline onset in longitudinal aging data.

Findings

Model avoids biases in simulated data

Successfully applied to real aging study data

Provides more accurate onset detection than classical models

Abstract

Change-point models are frequently considered when modeling phenomena where a regime shift occurs at an unknown time. In ageing research, these models are commonly adopted to estimate of the onset of cognitive decline. Yet commonly used 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 ageing 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 analysing results from memory tests from older adults who participated in the English Longitudinal Study of Ageing.