一个潜在类模型用于时间到事件结果和高维成像数据
Jiahui Feng1, Haolun Shi1, Ma Da2
1Department of Statistics and Actuarial Science, Simon Fraser University, Burnaby, British Columbia, Canada.
Statistics in medicine
|July 15, 2025
概括
这项研究引入了一种灵活的潜在类模型,使用结构性MRI来识别阿尔茨海默病 (AD) 亚型. 该模型揭示了针对个性化的AD治疗和研究的不同患者群体.
科学领域:
- 神经成像是一种神经成像.
- 生物统计学 生物统计学
- 计算生物学 计算生物学
背景情况:
- 结构磁共振成像 (MRI) 对于预测阿尔茨海默病 (AD) 风险和实现精准医学至关重要.
- 现有的模型往往缺乏灵活性来捕捉AD研究中的人口异质性和动态疾病进展.
研究的目的:
- 为在阿尔茨海默病研究中分析结构性MRI数据提出一种新的潜伏类模型.
- 解决人口异质性问题,并在AD进展中模拟不同共变量-生存结果关系.
- 开发一个强大的计算框架来实现拟议的统计模型.
主要方法:
- 开发了一种潜在类型模型,在三角化大脑图像域上结合了两种类型的线条.
- 设计了一个通用期望最大化 (EM) 算法,集成后勤回归和处罚比例危险模型.
- 该方法通过广泛的模拟研究得到验证,并应用于阿尔茨海默病神经成像计划 (ADNI) 数据集.
主要成果:
- 拟议的潜阶级模型有效地捕捉了阿尔茨海默病患者群体的异质性.
- 双变可以容纳复杂的,不规则的脑成像数据领域.
- 一般化的EM算法为模型实现提供了一种高效的计算方法.
结论:
- 开发的潜在类模型为使用结构性MRI进行阿尔茨海默病研究提供了灵活而强大的工具.
- 这种方法有助于识别不同的AD亚型或疾病阶段,进步精准医学.
- 对ADNI研究的应用证明了该模型在揭示疾病异质性的有用性.
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