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估计异质治疗效应与现实世界健康数据 - - 机器学习方法的范围审查

Michael Möller1, Eva-Maria Wild2, Winnie Tan1

  • 1Hamburg Center for Health Economics, University of Hamburg, Esplanade 36, 20354 Hamburg, Germany.

Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research
|February 5, 2026
PubMed
概括

机器学习 (ML) 方法使用现实世界数据 (RWD) 估计异质治疗效应 (HTEs). 定制条件平均治疗效应 (CATE) 方法正在增长,但医疗经济学研究的方法质量和报告需要改进.

关键词:
治疗效果的异质性治疗效果.机器学习 机器学习现实世界的数据数据.范围审查 范围审查

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