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Activity Number:
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360
- Advances and Challenges in Recent Diagnostic Research
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Type:
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Invited
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Date/Time:
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Thursday, August 12, 2021 : 12:00 PM to 1:50 PM
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Sponsor:
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Section on Medical Devices and Diagnostics
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Abstract #316621
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Title:
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Longitudinal Modeling of Disease Progression Biomarkers in the Latent Disease Timescale: Example of Alzheimer’s Disease and Related Disorders
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Author(s):
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Cécile Proust-Lima* and Jérémie Lespinasse and Carole Dufouil
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Companies:
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University of Bordeaux and Univ. Bordeaux, INSERM, BPH and Univ. Bordeaux, INSERM, BPH
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Keywords:
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biostatistics;
latent variables;
Longitudinal data;
multidimensional data;
mixed models;
neurodegenerative diseases
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Abstract:
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Alzheimer’s disease and related disorders (ADRD) are characterized by progressive changes in multiple components including brain atrophies and cognitive dysfunction. Understanding the sequence and timing of such deteriorations is paramount to refine patient stratification and facilitate earlier diagnosis. However, their modeling faces a fundamental statistical challenge: the timescale is not known. Usual timescales are inappropriate: time of clinical diagnosis is not an option as most deteriorations appear years before, time since inclusion does not have biological meaning, and chronological age induces too much inter-individual heterogeneity as people do not age similarly and ADRD onset may arise at various ages. We discuss how the mixed model theory can be used to realign individual trajectories into a common latent disease time while taking into account biomarkers specificities. We then illustrate the method to describe the progression of 12 biomarkers in the French clinic-based Memento study. Beyond the sequence of biomarker degradation, this methodology may evaluate at what stage of the disease an individual is by providing a prediction of his/her individual disease time.
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Authors who are presenting talks have a * after their name.
