Dynamic predictions in Bayesian functional joint models for longitudinal and time-to-event data: An application to Alzheimer's disease.
Dynamic predictions in Bayesian functional joint models for longitudinal and time-to-event data: An application to Alzheimer's disease.
复制标题
DOI：
10.1177/0962280217722177
复制
发表时间：
2019-03
影响因子：
2.3
通讯作者：
Luo S
中科院分区：
文献类型：
--
作者：
Li K;Luo S
在阿尔茨海默病（AD）的研究中，研究人员经常收集临床变量，事件历史和功能数据的重复测量。如果健康测量结果迅速恶化，患者可能会达到认知障碍的水平，并被诊断为痴呆症。根据收集的信息准确预测痴呆的时间有助于医生监测患者的疾病进展并做出早期知情的医疗决策。在这篇文章中，我们首先提出了一个功能性联合模型（FJM），以解释联合建模框架中纵向和生存子模型中的功能性预测因子。然后，我们开发了一个贝叶斯方法的参数估计和动态预测框架，预测受试者的未来结果轨迹和痴呆症的风险，基于他们的标量和功能测量。建议贝叶斯FJM提供了一个灵活的框架，将许多功能都在联合建模的纵向和生存数据和功能数据分析。我们提出的模型进行了评估的模拟研究，并应用于激励阿尔茨海默氏病神经影像学倡议（ADNI）的研究。
In the study of Alzheimer’s disease (AD), researchers often collect repeated measurements of clinical variables, event history, and functional data. If the health measurements deteriorate rapidly, patients may reach a level of cognitive impairment and are diagnosed as having dementia. An accurate prediction of the time to dementia based on the information collected is helpful for physicians to monitor patients’ disease progression and to make early informed medical decisions. In this article, we first propose a functional joint model (FJM) to account for functional predictors in both longitudinal and survival submodels in the joint modeling framework. We then develop a Bayesian approach for parameter estimation and a dynamic prediction framework for predicting the subjects’ future outcome trajectories and risk of dementia, based on their scalar and functional measurements. The proposed Bayesian FJM provides a flexible framework to incorporate many features both in joint modeling of longitudinal and survival data and in functional data analysis. Our proposed model is evaluated by a simulation study and is applied to the motivating Alzheimer’s Disease Neuroimaging Initiative (ADNI) study.
登录
查看更多内容
DOI：
10.1146/annurev-statistics-010814-020413
发表时间：
2015-01-01
期刊：
ANNUAL REVIEW OF STATISTICS AND ITS APPLICATION, VOL 2
影响因子：
--
作者：
Morris, Jeffrey S.
通讯作者：
Morris, Jeffrey S.
影响因子：
2.1
作者：
Gertheiss, Jan;Goldsmith, Jeff;Greven, Sonja
通讯作者：
Greven, Sonja
影响因子：
1.1
作者：
Greven S;Crainiceanu C;Caffo B;Reich D
通讯作者：
Reich D
影响因子：
1
作者：
Gellar JE;Colantuoni E;Needham DM;Crainiceanu CM
通讯作者：
Crainiceanu CM
影响因子：
11
作者：
Du, AT;Schuff, N;Weiner, MW
通讯作者：
Weiner, MW