Affiliations: [a] Department of Biostatistics, The University of Texas Health Science Center at Houston, Houston, TX, USA
| [b] Department of Neurology, The University of Texas Health Science Center at Houston, Houston, TX, USA
| [c] Department of Psychiatry, Neurology and Psychological and Brain Sciences, Carver College of Medicine, University of Iowa, Iowa City, IA, USA
Correspondence:
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Correspondence to: Sheng Luo, PhD, Department of Biostatistics, School of Public Health, The University of Texas Health Science Center at Houston, 1200 Herman Pressler Dr, Rm E815, Houston, TX 77030, USA. Tel.: +1 713 500 9554; E-mail: sheng.t.luo@uth.tmc.edu
Abstract: Background: Prediction of motor diagnosis in Huntington’s disease (HD) can be improved by incorporating other phenotypic and biological clinical measures in addition to cytosine-adenine-guanine (CAG) repeat length and age. Objective: The objective was to compare various clinical and biomarker trajectories for tracking HD progression and predicting motor conversion. Methods: Participants were from the PREDICT-HD study. We constructed a mixed-effect model to describe the change of measures while jointly modeling the process with time to HD diagnosis. The model was then used for subject-specific prediction. We employed the time-dependent receiver operating characteristic (ROC) method to assess the discriminating capability of the measures to identify high and low risk patients. The strongest predictor was used to illustrate the dynamic prediction of the disease risk and future trajectories of biomarkers for three hypothetical patients. Results: 1078 individuals were included in this analysis. Five longitudinal clinical and imaging measures were compared. The putamen volume had the best discrimination performance with area under the curve (AUC) ranging from 0.74 to 0.82 over time. The total motor score showed a comparable discriminative ability with AUC ranging from 0.69 to 0.78 over time. The model showed that decreasing putamen volume was a significant predictor of motor conversion. A web-based calculator was developed for implementing the methods. Conclusions: By jointly modeling longitudinal data with time-to-event outcomes, it is possible to construct an individualized dynamic event prediction model that renews over time with accumulating evidence. If validated, this could be a valuable tool to guide the clinician in predicting age of onset and potentially rate of progression.
Keywords: Longitudinal and survival data, individualized prediction, biomarkers, PREDICT-HD
