Epidemiology, Biostatistics and Prevention Institute, Center for Reproducible Science, University of Zurich, Zurich, Switzerland.
Biom J. 2024 Jan;66(1):e2200091. doi: 10.1002/bimj.202200091. Epub 2023 Mar 8.
Comparative simulation studies are workhorse tools for benchmarking statistical methods. As with other empirical studies, the success of simulation studies hinges on the quality of their design, execution, and reporting. If not conducted carefully and transparently, their conclusions may be misleading. In this paper, we discuss various questionable research practices, which may impact the validity of simulation studies, some of which cannot be detected or prevented by the current publication process in statistics journals. To illustrate our point, we invent a novel prediction method with no expected performance gain and benchmark it in a preregistered comparative simulation study. We show how easy it is to make the method appear superior over well-established competitor methods if questionable research practices are employed. Finally, we provide concrete suggestions for researchers, reviewers, and other academic stakeholders for improving the methodological quality of comparative simulation studies, such as preregistering simulation protocols, incentivizing neutral simulation studies, and code and data sharing.
比较模拟研究是基准统计方法的主要工具。与其他实证研究一样,模拟研究的成功取决于其设计、执行和报告的质量。如果不仔细和透明地进行,它们的结论可能会产生误导。在本文中,我们讨论了各种可能影响模拟研究有效性的有问题的研究实践,其中一些问题无法通过统计学期刊当前的出版流程来检测或预防。为了说明我们的观点,我们发明了一种新颖的预测方法,该方法没有预期的性能增益,并在预先注册的比较模拟研究中对其进行了基准测试。我们展示了如果采用有问题的研究实践,该方法如何轻易地显得优于成熟的竞争方法。最后,我们为研究人员、评论家和其他学术利益相关者提供了具体建议,以提高比较模拟研究的方法质量,例如预先注册模拟方案、激励中立的模拟研究以及代码和数据共享。