05. Overcoming Censoring in Predicting Huntington Disease Progression: a Comparative Modeling Study

Huntington disease (HD) is a genetically inherited neurodegenerative disease with progressively worsening symptoms including cognitive, psychological, and motor impairments. Accurately modeling time to HD diagnosis is essential for clinical trial design and patient treatment planning. Several statistical models have been proposed to model time to diagnosis, including Langbehn's model, the CAG-Age Product (CAP) model, the Prognostic Index Normed (PIN) model, and the Multivariate Risk Score (MRS) model. Because they differ in methodology, assumptions, and predictive accuracy, these models may yield conflicting predictions. These conflicts then create confusion for both patients and clinicians. We evaluate the theoretical foundations and empirical performance of the Langbehn, CAP, PIN, and MRS models via external validation using data from a new HD observational study, chosen for its independence from any model development, with the goal of informing model selection for future clinical trials. We begin with an assessment of each model's structure and assumptions to create a qualitative comparison of the models. We will consider practical factors such as covariate availability and model interpretability for clinical decision-making. Then, the models' empirical performance is evaluated and compared using this new HD observational study data. Performance metrics evaluating discrimination and calibration include Harrell's concordance statistic, the Brier score, calibration plots, and receiver operating characteristic curves. Although several models are available for predicting time to diagnosis, few studies have systematically compared their performance. This paper identifies methodological differences between these models and compares the models' empirical performance in terms of discrimination and calibration. Our findings aim to support and motivate the development and selection of robust models for use in clinical trial design and optimization in HD research.

Huntington disease

censored covariates

survival

predictive model 

Mid-Level

Knowledge

Women in Statistics and Data Science 2025