贝叶斯方法:提高临床前神经创伤统计能力的手段?

IF 1.8 Q3 CLINICAL NEUROLOGY
Neurotrauma reports Pub Date : 2024-07-16 eCollection Date: 2024-01-01 DOI:10.1089/neur.2024.0028
Peyton M Mueller, Abel Torres-Espín, Cole Vonder Haar
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引用次数: 0

摘要

神经创伤领域正在努力应对最近发现的复制危机的影响。因此,必须谨慎确定并执行最合适的统计分析。这将防止滥用研究资源,并确保结论合理且在数据范围之内。我们预计,贝叶斯统计方法将在未来几年得到越来越多的应用。贝叶斯方法将先验信念(或先验数据)整合到统计模型中,将历史信息和当前实验数据融合在一起。如果使用得当,这些方法可以提高检测实验组之间差异的能力(即统计能力)。不过,研究人员在实施或评估这些分析时,需要了解这些方法的优势和局限性。最终,使用贝叶斯方法可能会大大提高统计能力,但在确定和定义先验信念时需要谨慎。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Bayesian Methods: A Means of Improving Statistical Power in Preclinical Neurotrauma?

The field of neurotrauma is grappling with the effects of the recently identified replication crisis. As such, care must be taken to identify and perform the most appropriate statistical analyses. This will prevent misuse of research resources and ensure that conclusions are reasonable and within the scope of the data. We anticipate that Bayesian statistical methods will see increasing use in the coming years. Bayesian methods integrate prior beliefs (or prior data) into a statistical model to merge historical information and current experimental data. These methods may improve the ability to detect differences between experimental groups (i.e., statistical power) when used appropriately. However, researchers need to be aware of the strengths and limitations of such approaches if they are to implement or evaluate these analyses. Ultimately, an approach using Bayesian methodologies may have substantial benefits to statistical power, but caution needs to be taken when identifying and defining prior beliefs.

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来源期刊
CiteScore
2.40
自引率
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