Dataset Information

Dynamic Prediction of Motor Diagnosis in Huntington's Disease Using a Joint Modeling Approach.

ABSTRACT: 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.The objective was to compare various clinical and biomarker trajectories for tracking HD progression and predicting motor conversion.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.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.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.

SUBMITTER: Li K

PROVIDER: S-EPMC5505650 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Dynamic Prediction of Motor Diagnosis in Huntington's Disease Using a Joint Modeling Approach.

Li Kan K Furr-Stimming Erin E Paulsen Jane S JS Luo Sheng S

Journal of Huntington's disease 20170101 2

<h4>Background</h4>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.<h4>Objective</h4>The objective was to compare various clinical and biomarker trajectories for tracking HD progression and predicting motor conversion.<h4>Methods</h4>Participants were from the PREDICT-HD study. We constructed a mixed-effect model to describe the change ...[more]

PMID: 28582868

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Project description:BACKGROUND:Joint modeling is appropriate when one wants to predict the time to an event with covariates that are measured longitudinally and are related to the event. An underlying random effects structure links the survival and longitudinal submodels and allows for individual-specific predictions. Multiple time-varying and time-invariant covariates can be included to potentially increase prediction accuracy. The goal of this study was to estimate a multivariate joint model on several longitudinal observational studies of Huntington's disease, examine external validity performance, and compute individual-specific predictions for characterizing disease progression. Emphasis was on the survival submodel for predicting the hazard of motor diagnosis. METHODS:Data from four observational studies was analyzed: Enroll-HD, PREDICT-HD, REGISTRY, and Track-HD. A Bayesian approach to estimation was adopted, and external validation was performed using a time-varying AUC measure. Individual-specific cumulative hazard predictions were computed based on a simulation approach. The cumulative hazard was used for computing predicted age of motor onset and also for a deviance residual indicating the discrepancy between observed diagnosis status and model-based status. RESULTS:The joint model trained in a single study had very good performance in discriminating among diagnosed and pre-diagnosed participants in the remaining test studies, with the 5-year mean AUC?=?.83 (range .77-.90), and the 10-year mean AUC?=?.86 (range .82-.92). Graphical analysis of the predicted age of motor diagnosis showed an expected strong relationship with the trinucleotide expansion that causes Huntington's disease. Graphical analysis of the deviance-type residual revealed there were individuals who converted to a diagnosis despite having relatively low model-based risk, others who had not yet converted despite having relatively high risk, and the majority falling between the two extremes. CONCLUSIONS:Joint modeling is an improvement over traditional survival modeling because it considers all the longitudinal observations of covariates that are predictive of an event. Predictions from joint models can have greater accuracy because they are tailored to account for individual variability. These predictions can provide relatively accurate characterizations of individual disease progression, which might be important in the timing of interventions, determining the qualification for appropriate clinical trials, and general genotypic analysis.

Dataset Information

Dynamic Prediction of Motor Diagnosis in Huntington's Disease Using a Joint Modeling Approach.

Publications

Dynamic Prediction of Motor Diagnosis in Huntington's Disease Using a Joint Modeling Approach.

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