Comparing Risk Stratification of Existing Models for Time to Diagnosis of Huntington Disease

Thomas M. Menino Convention & Exhibition Center 

Huntington disease (HD) is a genetically inherited neurodegenerative disease with progressively worsening symptoms. Accurately modeling time to HD diagnosis is essential for clinical trial design and treatment planning. Langbehn's model, the CAG-Age Product model, the Prognostic Index Normed model, and the Multivariate Risk Score model have all been proposed for this task. Because they differ in methodology, assumptions, and accuracy, these models may yield conflicting predictions. Previous model comparisons are limited and those that exist could be misleading due to (i) testing the models on the same data used to train them and (ii) failing to account for high rates of right censoring (80%+) in performance metrics. We discuss the theoretical foundations of these models, offering comparisons about their practical feasibility. Further, we externally validate their risk stratification abilities using data from the ENROLL-HD study and performance metrics incorporating inverse probability of censoring weights and Kaplan-Meier adjustments. We also show these models can be used to estimate sample sizes for an HD clinical trial, emphasizing that estimates from previous work would lead to underpowered trials.

Concordance index

Censored covariates

Cox model

Inverse probability of censoring weighting

Neurodegenerative disease

ROC curves 

Biometrics Section