动态治疗方案中使用观察性医疗数据的方法:系统性审查
David Liang1, Animesh Kumar Paul2, Daniala L Weir1
1Division of Pharmacoepidemiology & Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences (UIPS), Utrecht University, Utrecht, the Netherlands.
本系统性审查分析了从观察健康数据中估计动态治疗方案 (DTR) 的方法. 强化学习和反事实模型很常见,大多数研究旨在改进现有的DTR.
科学领域:
- 医疗信息学 医疗信息学
- 生物统计学 生物统计学
- 医疗保健中的机器学习
背景情况:
- 动态治疗方案 (DTRs) 对个性化医疗至关重要,随着时间的推移指导治疗决策.
- 从观察性医疗保健数据中估计最佳的DTR带来了重大的方法学挑战.
- 现有的审查缺乏对当前DTR估计技术及其应用的全面综合.
研究的目的:
- 系统地审查和总结使用观察性医疗数据估计动态治疗方案 (DTR) 的方法.
- 评估各种DTR估计方法的优点,弱点和应用设置.
- 在DTR估计研究中确定共同的假设,验证策略和研究目标.
主要方法:
- 在PubMed和EMBASE数据库的系统文献搜索 (1950-2022年).
- 包括观察性研究评估医疗治疗和估计DTRs.
- 方法的分类,包括强化学习,反事实模型,分类和g方法.
主要成果:
- 83项研究符合纳入标准;强化学习 (44.6%) 和反事实模型 (18.1%) 是普遍存在的.
- 大多数研究 (71.1%) 旨在改进现有的DTR,而不是复制人类专家 (28.9%).
- 通常报告的假设 (65.1%) 包括可交换性和积极性;在50.6%的研究中注意到时间变化的混因素.
结论:
- 强化学习和基于反事实的模型是观察数据中DTR估计的主要方法.
- 未来的研究应该专注于解决时间变化的混因素和估计条件平均治疗效果.
- 使用各种方法进行强大的验证,包括模拟和真实数据,对于可靠的DTR估计至关重要.
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