Center for Health care Organization and Implementation Research, Bedford VA Medical Center, 200 Springs Road (152), Bedford, MA 01730, USA; Department of Health Policy and Management, Boston University School of Public Health, 715 Albany Street, Talbot Building, Boston, MA 02118, USA.
Center for Health care Organization and Implementation Research, Bedford VA Medical Center, 200 Springs Road (152), Bedford, MA 01730, USA; Department of Quantitative Health Sciences, University of Massachusetts Medical School, 55 N. Lake Avenue, Worcester, MA 01655, USA.
J Clin Epidemiol. 2014 Aug;67(8):850-7. doi: 10.1016/j.jclinepi.2014.03.012. Epub 2014 May 13.
Procedures for controlling the false positive rate when performing many hypothesis tests are commonplace in health and medical studies. Such procedures, most notably the Bonferroni adjustment, suffer from the problem that error rate control cannot be localized to individual tests, and that these procedures do not distinguish between exploratory and/or data-driven testing vs. hypothesis-driven testing. Instead, procedures derived from limiting false discovery rates may be a more appealing method to control error rates in multiple tests.
Controlling the false positive rate can lead to philosophical inconsistencies that can negatively impact the practice of reporting statistically significant findings. We demonstrate that the false discovery rate approach can overcome these inconsistencies and illustrate its benefit through an application to two recent health studies.
The false discovery rate approach is more powerful than methods like the Bonferroni procedure that control false positive rates. Controlling the false discovery rate in a study that arguably consisted of scientifically driven hypotheses found nearly as many significant results as without any adjustment, whereas the Bonferroni procedure found no significant results.
Although still unfamiliar to many health researchers, the use of false discovery rate control in the context of multiple testing can provide a solid basis for drawing conclusions about statistical significance.
在进行多项假设检验时，控制假阳性率的程序在健康和医学研究中很常见。此类程序（尤其是 Bonferroni 调整）存在一个问题，即无法将错误率控制本地化到单个测试中，并且这些程序无法区分探索性和/或数据驱动的测试与假设驱动的测试。相反，源自限制假发现率的程序可能是控制多项测试中错误率的一种更具吸引力的方法。
控制假阳性率可能导致哲学上的不一致，从而对报告具有统计学意义的发现的实践产生负面影响。我们证明，错误发现率方法可以克服这些不一致，并通过对两个最近的健康研究的应用来说明其益处。
错误发现率方法比控制假阳性率的 Bonferroni 等方法更强大。在一项据称由科学驱动的假设组成的研究中，控制错误发现率可以发现与不进行任何调整时几乎相同数量的显著结果，而 Bonferroni 程序则没有发现显著结果。
尽管许多健康研究人员对此仍然不熟悉，但在多项测试的背景下使用错误发现率控制可以为得出关于统计显著性的结论提供坚实的基础。
