代谢组学数据的统计报告：高通量 NMR 平台和流行病学应用的经验。
Statistical reporting of metabolomics data: experience from a high-throughput NMR platform and epidemiological applications.
机构信息
Computational Systems Biology Program, Precision Medicine Theme, South Australian Health and Medical Research Institute, Adelaide, Australia.
Folkhälsan Institute of Genetics, Folkhälsan Research Center, Helsinki, Finland.
出版信息
Metabolomics. 2019 Dec 10;16(1):5. doi: 10.1007/s11306-019-1626-y.
INTRODUCTION
Meta-analysis is the cornerstone of robust biomedical evidence.
OBJECTIVES
We investigated whether statistical reporting practices facilitate metabolomics meta-analyses.
METHODS
A literature review of 44 studies that used a comparable platform.
RESULTS
Non-numeric formats were used in 31 studies. In half of the studies, less than a third of all measures were reported. Unadjusted P-values were missing from 12 studies and exact P-values from 9 studies.
CONCLUSION
Reporting practices can be improved. We recommend (i) publishing all results as numbers, (ii) reporting effect sizes of all measured metabolites and (iii) always reporting unadjusted exact P-values.
介绍
元分析是稳健的生物医学证据的基石。
目的
我们研究了统计报告实践是否有助于代谢组学元分析。
方法
对使用可比平台的 44 项研究进行文献回顾。
结果
31 项研究使用非数值格式。在一半的研究中，不到三分之一的所有指标都被报告了。12 项研究缺失未调整 P 值，9 项研究缺失确切 P 值。
结论
报告实践可以改进。我们建议（i）以数字形式发布所有结果，（ii）报告所有测量代谢物的效应大小，（iii）始终报告未经调整的精确 P 值。
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