治疗效果分析异质性的结果风险模型开发:非参数机器学习方法和半参数统计方法的比较
Edward Xu1, Joseph Vanghelof2, Yiyang Wang1
1Jarvis College of Computing and Digital Media, DePaul University, Chicago, IL, United States of America.
BMC medical research methodology
|July 23, 2024
概括
预测患者风险的不同方法影响治疗效果异质性 (HTE) 分析. 相对危险模型显示,不同于机器学习模型,在各个子组中绝对风险降低 (ARR) 的显著差异. 仔细选择分组技术对于HTE研究至关重要.
科学领域:
- 临床试验方法论 临床试验方法论
- 生物统计学 生物统计学
- 医疗保健中的机器学习
背景情况:
- 临床试验中治疗效果 (HTE) 的异质性表明治疗效果在参与者之间可能有所不同.
- 量化HTE通常涉及根据参与者对结果的风险分组参与者.
- 由于外部模型有限,内部开发的风险预测模型经常用于子组识别.
研究的目的:
- 为了比较不同的方法来生成内部开发的结果风险预测模型,用于HTE分析中的参与者分组.
- 评估各种分组技术对随机对照试验中治疗效果评估的影响.
主要方法:
- 使用了三种方法:现有的比例危险模型和两个机器学习模型 (决策树和随机森林).
- 根据预测复合结果的风险,ASPREE试验 (ASPirin in Reducing Events in the Elderly) 的参与者被分组.
- 该研究比较了从这些模型中的亚组分区在阿司匹林与安慰剂的HTE分析中,评估了5年绝对风险降低 (ARR) 和危险比率.
主要成果:
- 比例危险模型生成了5个子组;决策树生成了6;随机森林生成了5个子组.
- 比例危险模型和随机森林模型确定了具有阿司匹林显著5年ARR的最高风险组,而决策树具有更宽的置信区间.
- 科克兰的Q测试仅在比例危险模型中显示出每个子组的显著ARR变化;在任何模型中,危险比率在每个子组中都没有显著变化.
结论:
- 对结果风险分组的内部模型的选择显著影响HTE分析.
- 模型选择应考虑可解释性,预测不确定性,过拟合风险和数据假设.
- 需要在其他临床试验中进行进一步的研究,以确定为HTE分析选择结果风险预测建模技术的指导.
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