Goldilocks Rounding: Achieving Balance Between Accuracy and Parsimony in the Reporting of Relative Effect Estimates.

IF 2.4 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Cancer Informatics Pub Date : 2021-01-05 eCollection Date: 2021-01-01 DOI:10.1177/1176935120985132
Jimmy T Efird
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引用次数: 7

Abstract

Researchers often report a measure to several decimal places more than what is sensible or realistic. Rounding involves replacing a number with a value of lesser accuracy while minimizing the practical loss of validity. This practice is generally acceptable to simplify data presentation and to facilitate the communication and comparison of research results. Rounding also may reduce spurious accuracy when the extraneous digits are not justified by the exactness of the recording instrument or data collection procedure. However, substituting a more explicit or simpler representation for an original measure may not be practicable or acceptable if an adequate degree of accuracy is not retained. The error introduced by rounding exact numbers may result in misleading conclusions and the interpretation of study findings. For example, rounding the upper confidence interval for a relative effect estimate of 0.996 to 2 decimal places may obscure the statistical significance of the result. When presenting the findings of a study, authors need to be careful that they do not report numbers that contain too few significant digits. Equally important, they should avoid providing more significant figures than are warranted to convey the underlying meaning of the result.

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金发姑娘四舍五入:在报告相对影响估计时达到准确与节俭之间的平衡。
研究人员报告的测量值往往比实际值高出小数点后几位。舍入是指用精度较低的值替换一个数字,同时尽量减少有效性的实际损失。这种做法通常是可以接受的,以简化数据的呈现,并促进研究结果的交流和比较。当记录仪器或数据收集程序的准确性不能证明多余的数字时,舍入也可以减少虚假的准确性。但是,如果不能保持足够的准确性,用更明确或更简单的表示代替原始度量可能是不可行的或不可接受的。精确数字四舍五入带来的误差可能导致误导性结论和对研究结果的解释。例如,将0.996的相对效应估计的上置信区间四舍五入到小数点后2位可能会模糊结果的统计显著性。在展示研究结果时,作者需要小心,不要报告包含太少有效数字的数字。同样重要的是,他们应该避免提供比所保证的更重要的数字来传达结果的潜在意义。
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来源期刊
Cancer Informatics
Cancer Informatics Medicine-Oncology
CiteScore
3.00
自引率
5.00%
发文量
30
审稿时长
8 weeks
期刊介绍: The field of cancer research relies on advances in many other disciplines, including omics technology, mass spectrometry, radio imaging, computer science, and biostatistics. Cancer Informatics provides open access to peer-reviewed high-quality manuscripts reporting bioinformatics analysis of molecular genetics and/or clinical data pertaining to cancer, emphasizing the use of machine learning, artificial intelligence, statistical algorithms, advanced imaging techniques, data visualization, and high-throughput technologies. As the leading journal dedicated exclusively to the report of the use of computational methods in cancer research and practice, Cancer Informatics leverages methodological improvements in systems biology, genomics, proteomics, metabolomics, and molecular biochemistry into the fields of cancer detection, treatment, classification, risk-prediction, prevention, outcome, and modeling.
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