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

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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)

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.

Suggested Citation

  • Franklin Fernando Massa & Marco Scavino & Graciela Muniz-Terrera, 2025. "A Bayesian Non-Linear Mixed-Effects Model for Accurate Detection of the Onset of Cognitive Decline in Longitudinal Aging Studies," Stats, MDPI, vol. 8(3), pages 1-16, August.
    • Handle: RePEc:gam:jstats:v:8:y:2025:i:3:p:74-:d:1726497
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