Open AccessModeling Variability in the Progression of Huntington's Disease A Novel Modeling Approach Applied to Structural Imaging Markers from TRACK‐HD
Author(s) -
Warner JH,
Sampaio C
Publication year - 2016
Publication title -
cpt: pharmacometrics and systems pharmacology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.53
H-Index - 37
ISSN - 2163-8306
DOI - 10.1002/psp4.12097
Subject(s) - covariate , random walk , huntington's disease , huntingtin , population , kalman filter , selection (genetic algorithm) , regression , disease , biology , statistics , genetics , mathematics , computer science , artificial intelligence , medicine , environmental health
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.