家庭数据的三变联合建模与纵向计数,反复事件和终端事件与林奇综合征的应用
Jingwei Lu1, Grace Y Yi1,2, Denis Rustand3
1Department of Statistical and Actuarial Sciences, The University of Western Ontario, London, Canada.
Statistics in medicine
|September 15, 2024
概括
这项研究引入了一种联合模型,用于分析林奇综合征 (LS) 家庭的结直肠癌 (CRC) 风险. 该模型将聚数和结肠镜查频率与CRC发生联系起来,改善针对性干预的风险评估.
科学领域:
- 生物统计学 生物统计学
- 医学统计 医学统计
- 流行病学 流行病学
背景情况:
- 林奇综合征 (LS) 家庭面临着高结肠直肠癌 (CRC) 风险.
- 息肉负担和结肠镜查频率影响CRC风险.
- 纵向,复发和终端事件的联合建模对于家庭数据至关重要.
研究的目的:
- 为分析纵向计数数据 (多胞体),反复事件 (结肠镜检查) 和终端事件 (CRC) 提出一个聚类三变联合模型.
- 为了考虑到零膨胀和过分散的计数数据存在的个人特异性和家庭特异性随机效应.
- 评估查对聚合体检测和随后家族内CRC风险的影响.
主要方法:
- 使用集成嵌套拉普拉斯近似 (INLA) 算法开发了贝叶斯估计的潜在高斯模型.
- 应用于18个LS家族的三变组合模型,分析CRC作为终端事件,结肠镜作为反复事件,以及聚数作为纵向数据.
- 整合了特定于个人和特定于家庭的随机效应,以处理数据依赖.
主要成果:
- 拟议的三变联合模型与两变模型相比,显示出更好的匹配.
- 该分析强调了在以家庭为基础的研究中包括集群效应 (家庭特异效应) 的重要性.
- 在个人和家庭中检测多和CRC风险的量化异质性.
结论:
- 三元联合模型有效地分析复杂的家庭健康数据,整合了多细胞数量,查事件和癌症发生率.
- 忽视集群效应可能导致家庭研究中的分析不准确.
- 该模型有助于识别高风险个人和家庭,以定制,密集的查策略,以减轻CRC风险.
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