期刊
ENTROPY
卷 25, 期 4, 页码 -出版社
MDPI
DOI: 10.3390/e25040691
关键词
meta-analysis; cherry-picking studies; selection bias; adversarial meta-analysis; inclusion; exclusion criteria
We investigate selection bias in meta-analyses by assuming the existence of researchers (meta-analysts) who selectively choose a subset of studies based on arbitrary inclusion and/or exclusion criteria to achieve desired results. Regardless of the actual effectiveness of a treatment, our theoretical analysis shows that meta-analysts can falsely obtain (non)significant overall treatment effects when the number of studies is sufficiently large. We validate our theoretical findings through extensive simulation experiments and practical clinical examples, demonstrating the potential for cherry-picking in standard methods for meta-analyses.
We study selection bias in meta-analyses by assuming the presence of researchers (meta-analysts) who intentionally or unintentionally cherry-pick a subset of studies by defining arbitrary inclusion and/or exclusion criteria that will lead to their desired results. When the number of studies is sufficiently large, we theoretically show that a meta-analysts might falsely obtain (non)significant overall treatment effects, regardless of the actual effectiveness of a treatment. We analyze all theoretical findings based on extensive simulation experiments and practical clinical examples. Numerical evaluations demonstrate that the standard method for meta-analyses has the potential to be cherry-picked.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据