机器学习方法来评估随机对照试验中的异质治疗效果:一个范围审查
Kosuke Inoue1, Motohiko Adomi2, Orestis Efthimiou3
1Department of Social Epidemiology, Graduate School of Medicine, Kyoto University, Kyoto, Japan; Hakubi Center, Kyoto University, Kyoto, Japan.
Journal of clinical epidemiology
|September 21, 2024
概括
本综述列出了统计和机器学习算法,用于在随机对照试验 (RCT) 中估计异质治疗效应 (HTEs). 因果森林和贝叶斯因果森林是经常用于识别个性化治疗效果的方法.
科学领域:
- 生物统计学 生物统计学
- 机器学习 机器学习
- 临床试验 临床试验
背景情况:
- 在随机对照试验 (RCT) 中估计异质治疗效应 (HTEs) 对个性化医学至关重要.
- 缺乏对HTE评估现有的统计和机器学习 (ML) 算法的全面审查.
- 识别个性化治疗效应需要先进的分析方法.
研究的目的:
- 在临床RCT数据中对HTEs进行识别的统计和ML方法进行目录和概述.
- 总结这些HTE算法的实际应用.
- 为该领域的研究人员和分析师提供资源.
主要方法:
- 使用MEDLINE和Embase数据库进行了范围审查.
- 确定了2010年至2022年间发表的研究,在RCT数据中使用先进的统计和ML方法评估HTEs.
- 使用预先指定的搜索术语来确保系统的方法.
主要成果:
- 确定了32项研究,其中17项应用现有算法,15项扩展或提出新的算法.
- 常用的算法包括因果森林 (7项研究) 和贝叶斯因果森林 (4项研究).
- 应用最常见的是心脏病学 (6项研究) 和精神病学 (4项研究).
结论:
- 这次审查确定并概述了在RCT数据中对高耐久性瘤和个性化治疗效应的各种算法.
- 随着新算法的日益普及,需要根据模型性能和实际实用性进行仔细的选择.
- 在选择HTE识别方法时,分析师应考虑特定的应用环境.
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