Analysis validation has been neglected in the Age of Reproducibility
Analysis validation has been neglected in the Age of Reproducibility
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DOI：
10.1371/journal.pbio.3000070
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发表时间：
2018-12-01
期刊：
影响因子：
9.8
通讯作者：
Stapleton, Ann E.
中科院分区：
文献类型：
--
作者：
Lotterhos, Kathleen E.;Moore, Jason H.;Stapleton, Ann E.
越来越复杂的统计模型被用于分析生物数据。最近的评论集中在对给定数据集计算相同结果的能力（再现性）上。我们认为，可重复的统计分析不一定有效，因为每个生物数据集中都有独特的非独立性模式。我们主张应该用已知事实模拟来评估分析，以捕捉生物现实，我们称之为“分析验证”的过程。我们回顾验证的过程，并建议验证项目应该满足的标准。我们发现不同的科学领域在历史上未能满足所有标准，我们建议在培训和实践中实施有意义的验证的方法。
Increasingly complex statistical models are being used for the analysis of biological data. Recent commentary has focused on the ability to compute the same outcome for a given dataset (reproducibility). We argue that a reproducible statistical analysis is not necessarily valid because of unique patterns of nonindependence in every biological dataset. We advocate that analyses should be evaluated with known-truth simulations that capture biological reality, a process we call "analysis validation." We review the process of validation and suggest criteria that a validation project should meet. We find that different fields of science have historically failed to meet all criteria, and we suggest ways to implement meaningful validation in training and practice.