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研究和临床前验证的可重复性:问题与解决方案。

Reproducibility of research and preclinical validation: problems and solutions.

机构信息

Section of Medical Oncology, Yale Cancer Center, Yale School of Medicine, 333 Cedar Street, New Haven, CT 06520-8032, USA.

出版信息

Nat Rev Clin Oncol. 2013 Dec;10(12):720-4. doi: 10.1038/nrclinonc.2013.171. Epub 2013 Oct 1.

DOI:10.1038/nrclinonc.2013.171
PMID:24080600
Abstract

Lack of reproducibility in the scientific and lay literature of many scientific reports is an increasing concern, as are the high rates of failure to validate highly promising preclinical observations in clinical trials. There are many technical reasons why experimental results, particularly in cancer research, cannot be reproduced, including unrecognized variables in the complex experimental model, poor documentation of procedures, selective reporting of the most-positive findings, misinterpretation of technical noise as biological signal and, in the most extreme cases, fabrication of data. We suggest that cognitive biases in research and flaws in the academic incentive system also contribute to the publication of immature results. Recognition of these factors, which are often not discussed, provides additional strategies to improve reproducibility. We suggest that in addition to establishing better standards of data presentation and creating venues for publication of negative results, some changes to the grant submission and funding system could further improve the reproducibility of research findings.

摘要

许多科学报告的科学和通俗文献中缺乏可重复性,临床研究中未能验证有前途的临床前观察结果的高比例也令人担忧。实验结果无法重现的原因有很多,包括复杂实验模型中未被识别的变量、程序记录不佳、选择性报告最积极的发现、将技术噪声误判为生物信号,以及在最极端的情况下伪造数据。我们认为,研究中的认知偏差和学术激励系统中的缺陷也导致了不成熟结果的发表。认识到这些通常未被讨论的因素为提高可重复性提供了额外的策略。我们建议,除了建立更好的数据展示标准和为负面结果的发表创造场所外,对资助提交和资助系统进行一些更改可以进一步提高研究结果的可重复性。

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Nature. 2012 Oct 11;490(7419):187-91. doi: 10.1038/nature11556.
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Beware the creeping cracks of bias.谨防偏见的细微裂缝。
Nature. 2012 May 9;485(7397):149. doi: 10.1038/485149a.
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Drug development: Raise standards for preclinical cancer research.药物研发:提高临床前癌症研究标准。
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Preclinical assessment for translation to humans: The PATH approach for assessing supporting evidence for early-phase trials and innovative care.临床前评估向人体转化:评估早期试验和创新护理支持证据的 PATH 方法。
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Ensemble learning for integrative prediction of genetic values with genomic variants.基于基因组变异的遗传值综合预测的集成学习。
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