结合影像生物标志物的阿尔茨海默病进展的半参数函数多状态模型
Semiparametric Functional Multistate Modeling of Alzheimer's Disease Progression with Imaging Biomarkers
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中文总结 AI 辅助
该研究针对阿尔茨海默病进展预测，提出结合影像生物标志物的半参数函数多状态模型，经模拟和ADNI数据验证，其预测性能优于对比方法。
中文摘要 AI 辅助
医学影像为预测阿尔茨海默病进展提供了丰富信息，但现有基于影像的方法通常聚焦于单一生存终点，且将转移时间视为精确观测或右删失。受阿尔茨海默病神经影像倡议（ADNI）的启发，我们开发了一个预测框架，将疾病进展表示为间歇观测的多状态过程，其转移时间为区间删失，并可从任意当前疾病状态预测未来进展。我们将影像生物标志物作为函数协变量纳入半参数比例强度模型，结合函数主成分分析与非参数极大伪似然估计。我们还开发了轮廓得分检验，用于评估影像协变量与多状态过程之间的整体关联。我们建立了所提估计量和检验统计量的渐近性质，模拟研究表明其具有良好的有限样本性能。在ADNI应用中，基线侧脑室形态与阿尔茨海默病进展存在强关联，所提函数多状态模型在对比方法中实现了最佳的整体预测性能。
英文摘要
Medical imaging provides rich information for predicting Alzheimer's disease progression, but existing imaging-based methods typically focus on a single survival endpoint and treat transition times as exactly observed or right-censored. Motivated by the Alzheimer's Disease Neuroimaging Initiative (ADNI), we develop a predictive framework that represents disease progression as an intermittently observed multistate process with interval-censored transition times and predicts future progression from any current disease state. We incorporate imaging biomarkers as functional covariates in a semiparametric proportional intensity model and combine functional principal component analysis with nonparametric maximum pseudo likelihood estimation. We further develop a profile score test for assessing the overall association between the imaging covariate and the multistate process. We establish the asymptotic properties of the proposed estimators and test statistic, and simulation studies demonstrate satisfactory finite-sample performance. In the ADNI application, baseline lateral ventricular morphology is strongly associated with Alzheimer's disease progression. The proposed functional multistate model also achieves the best overall predictive performance among the competing methods.
