Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina.
Department of Statistics, North Carolina State University, Raleigh, North Carolina.
Stat Med. 2023 May 10;42(10):1492-1511. doi: 10.1002/sim.9683. Epub 2023 Feb 20.
Alzheimer's Disease (AD) is the leading cause of dementia and impairment in various domains. Recent AD studies, (ie, Alzheimer's Disease Neuroimaging Initiative (ADNI) study), collect multimodal data, including longitudinal neurological assessments and magnetic resonance imaging (MRI) data, to better study the disease progression. Adopting early interventions is essential to slow AD progression for subjects with mild cognitive impairment (MCI). It is of particular interest to develop an AD predictive model that leverages multimodal data and provides accurate personalized predictions. In this article, we propose a multivariate functional mixed model with MRI data (MFMM-MRI) that simultaneously models longitudinal neurological assessments, baseline MRI data, and the survival outcome (ie, dementia onset) for subjects with MCI at baseline. Two functional forms (the random-effects model and instantaneous model) linking the longitudinal and survival process are investigated. We use Markov Chain Monte Carlo (MCMC) method based on No-U-Turn Sampling (NUTS) algorithm to obtain posterior samples. We develop a dynamic prediction framework that provides accurate personalized predictions of longitudinal trajectories and survival probability. We apply MFMM-MRI to the ADNI study and identify significant associations among longitudinal outcomes, MRI data, and the risk of dementia onset. The instantaneous model with voxels from the whole brain has the best prediction performance among all candidate models. The simulation study supports the validity of the estimation and dynamic prediction method.
阿尔茨海默病（AD）是痴呆症和各种功能障碍的主要原因。最近的 AD 研究（例如，阿尔茨海默病神经影像学倡议（ADNI）研究）收集了多模态数据，包括纵向神经评估和磁共振成像（MRI）数据，以更好地研究疾病进展。对于轻度认知障碍（MCI）患者，采用早期干预措施对于减缓 AD 进展至关重要。开发一种利用多模态数据并提供准确个性化预测的 AD 预测模型尤为重要。在本文中，我们提出了一种基于 MRI 数据的多元功能混合模型（MFMM-MRI），该模型同时对 MCI 患者的纵向神经评估、基线 MRI 数据和生存结局（即痴呆发作）进行建模。研究了两种链接纵向和生存过程的功能形式（随机效应模型和瞬时模型）。我们使用基于无翻转抽样（NUTS）算法的马尔可夫链蒙特卡罗（MCMC）方法来获得后验样本。我们开发了一个动态预测框架，可提供纵向轨迹和生存概率的准确个性化预测。我们将 MFMM-MRI 应用于 ADNI 研究，并确定了纵向结果、MRI 数据和痴呆发作风险之间的显著关联。来自整个大脑体素的瞬时模型在所有候选模型中具有最佳的预测性能。模拟研究支持了估计和动态预测方法的有效性。
