Modeling Variability in the Progression of Huntington's Disease A Novel Modeling Approach Applied to Structural Imaging Markers from TRACK‐HD

We present a novel, general class of disease progression models for Huntington's disease (HD), a neurodegenerative disease caused by a cytosine‐adenine‐guanine (CAG) triplet repeat expansion on the huntingtin gene. Models are fit to a selection of structural imaging markers from the TRACK 36‐mo...

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Bibliographic Details
Published in:CPT: pharmacometrics and systems pharmacology 2016-08, Vol.5 (8), p.437-445
Main Authors: Warner, JH, Sampaio, C
Format: Article
Language:English
Subjects:
Age
Age Factors
Aging
Clinical outcomes
Disease Progression
Humans
Huntington Disease - diagnostic imaging
Huntington Disease - genetics
Huntingtons disease
Kalman filters
Models, Neurological
Original
Pathology
Trinucleotide Repeat Expansion - genetics
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Summary:We present a novel, general class of disease progression models for Huntington's disease (HD), a neurodegenerative disease caused by a cytosine‐adenine‐guanine (CAG) triplet repeat expansion on the huntingtin gene. Models are fit to a selection of structural imaging markers from the TRACK 36‐month database. The models are of mixed effects type and should be useful in predicting any continuous marker of HD state as a function of age and CAG length (the genetic factor that drives HD pathology). The effects of age and CAG length are modeled using flexible regression splines. Variability not accounted for by age, CAG length, or covariates is modeled using terms that represent measurement error, population variability (random slopes/intercepts), and variability due to the dynamics of the disease process (random walk terms). A Kalman filter is used to estimate variances of the random walk terms.
ISSN:2163-8306
2163-8306
DOI:10.1002/psp4.12097