Mueller Peyton M, Torres-Espín Abel, Vonder Haar Cole
Department of Neuroscience, Injury and Recovery Laboratory, Ohio State University, Columbus, Ohio, USA.
School of Public Health Sciences, University of Waterloo, Waterloo, Canada.
Neurotrauma Rep. 2024 Jul 16;5(1):699-707. doi: 10.1089/neur.2024.0028. eCollection 2024.
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.
神经创伤领域正在应对最近发现的复制危机所带来的影响。因此，必须谨慎识别并进行最恰当的统计分析。这将防止研究资源的滥用，并确保结论合理且在数据范围内。我们预计贝叶斯统计方法在未来几年将得到越来越广泛的应用。贝叶斯方法将先验信念（或先验数据）整合到统计模型中，以融合历史信息和当前实验数据。如果使用得当，这些方法可能会提高检测实验组之间差异的能力（即统计功效）。然而，研究人员在实施或评估这些分析时需要了解此类方法的优点和局限性。最终，使用贝叶斯方法的途径可能会对统计功效带来实质性益处，但在识别和定义先验信念时需要谨慎。
