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 (CAP) model, the Prognostic Index Normed (PIN) model, and the Multivariate Risk Score (MRS) model have all been proposed for this task. However, differing in methodology, assumptions, and accuracy, these models may yield conflicting predictions. Few studies have systematically compared these models' performance, and those that have 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 the four most common models of time to HD diagnosis, offering intuitive comparisons about their practical feasibility. Further, we externally validate their risk stratification abilities using data from the ENROLL-HD study and performance metrics that adjust for censoring. Our findings guide the selection of a model for HD clinical trial design. The MRS model, which incorporates the most covariates, performed the best. However, the simpler CAP and PIN models were not far behind and may be logistically simpler to adopt. We also show how these models can be used to estimate sample sizes for an HD clinical trial, emphasizing that previous estimates would lead to underpowered trials.

翻译：亨廷顿病（HD）是一种遗传性神经退行性疾病，其症状随时间逐渐恶化。准确建模HD诊断时间对于临床试验设计和治疗规划至关重要。为此，Langbehn模型、CAG-年龄乘积（CAP）模型、归一化预后指数（PIN）模型以及多变量风险评分（MRS）模型均被提出用于此任务。然而，这些模型在方法、假设和准确性上存在差异，可能导致相互矛盾的预测结果。目前系统比较这些模型性能的研究较少，且已有研究可能因以下原因产生误导：（i）在用于训练模型的相同数据上测试模型性能；（ii）在性能指标中未考虑高达80%以上的高右删失率。本文讨论了四种最常见的HD诊断时间模型的理论基础，并对其实际可行性进行了直观比较。进一步，我们利用ENROLL-HD研究的数据以及调整了删失影响的性能指标，对这些模型的风险分层能力进行了外部验证。研究结果为HD临床试验设计中的模型选择提供了指导。纳入最多协变量的MRS模型表现最佳，但更简化的CAP和PIN模型性能相近，且在实施上可能更易于采用。我们还展示了如何利用这些模型估算HD临床试验的样本量，并强调先前的估计可能导致试验效力不足。