在集群随机对照试验研究中的纵向和生存结果的贝叶斯多级联合建模
Yixiu Liu1, Depeng Jiang1, Mahmoud Torabi1
1College of Community and Global Health, University of Manitoba, Winnipeg, Manitoba, Canada.
Statistics in medicine
|January 22, 2026
概括
一个新的多级联合模型 (MJM) 准确地分析来自集群随机对照试验 (CRCT) 的集群数据. 这种方法通过考虑层次结构来改善心理健康干预分析,防止偏见的结果.
科学领域:
- 生物统计学 生物统计学
- 公共卫生 公共卫生
- 发展心理学 发展心理学
背景情况:
- 集群随机对照试验 (CRCT) 对于群体级干预至关重要,但需要考虑固有的多层次数据结构的方法.
- 对于纵向和生存数据的传统联合模型 (JM) 无法充分处理CRCT中常见的集群.
- 忽视CRCT中的层次数据结构可能会导致偏见的推断和低估的干预效应.
研究的目的:
- 引入一种新的多层次关节模型 (MJM),旨在分析CRCT数据中的相关纵向和生存结果.
- 评估PAX好行为游戏的有效性,这是一项基于课堂的心理健康干预,使用拟议的MJM.
- 通过在各种数据条件下进行模拟研究来评估MJM的性能.
主要方法:
- 开发和应用一个多层联合模型 (MJM) 来分析来自大型CRCT的数据.
- 在MJM同时建模心理健康轨迹 (纵向) 和时间到精神障碍诊断 (生存).
- 进行了模拟研究,以评估MJM在不同审查率,集群大小和差异组件上的表现.
主要成果:
- 该PAX计划在心理健康轨迹上显著改善,并降低了精神障碍诊断的风险.
- 与传统的JM相比,MJM提供了更准确的参数估计和标准错误.
- 模拟结果证实,忽视CRCT数据中的聚类导致偏差估计和低估干预效应.
结论:
- 拟议的MJM是一个强大而灵活的框架,用于分析来自CRCT的复杂集群数据.
- 对等级结构的记账对于准确评估基于群体的干预措施至关重要.
- 该MJM提高了公共卫生和教育干预研究发现的可靠性.
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