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False discovery rate control is a recommended alternative to Bonferroni-type adjustments in health studies
Mark E Glickman1, Sowmya R Rao2, Mark R Schultz3
1Center 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.
Objectives:
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.
Study Design And Setting:
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.
Results:
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.
Conclusion:
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.
Insights
The false discovery rate (FDR) approach offers a more powerful alternative to traditional methods like the Bonferroni adjustment for controlling errors in multiple hypothesis testing. FDR provides a more robust framework for drawing conclusions from health and medical studies.
Area of Science:
- Biostatistics
- Medical Research
- Statistical Significance
Background:
- Traditional methods for controlling false positive rates in multiple hypothesis testing, such as the Bonferroni adjustment, have limitations.
- These limitations include a lack of localization to individual tests and failure to differentiate between exploratory and hypothesis-driven testing.
- Philosophical inconsistencies can arise from controlling the overall false positive rate, impacting the reporting of statistically significant findings.
Purpose of the Study:
- To introduce and advocate for the use of false discovery rate (FDR) control methods in health and medical research.
- To demonstrate how FDR control can overcome the limitations and inconsistencies associated with traditional false positive rate control procedures.
- To illustrate the practical benefits of FDR control through real-world health study applications.
Main Methods:
- The study evaluates the application of false discovery rate control methods.
- Compares the power and effectiveness of FDR control against traditional methods like the Bonferroni procedure.
- Applies FDR control to two recent health studies to demonstrate its utility.
Main Results:
- The false discovery rate approach demonstrates greater statistical power compared to methods like the Bonferroni procedure.
- In a study with scientifically driven hypotheses, FDR control identified nearly as many significant results as unadjusted analyses.
- Conversely, the Bonferroni procedure identified no significant results in the same study, highlighting its conservative nature.
Conclusions:
- False discovery rate control offers a statistically sound basis for drawing conclusions in multiple testing scenarios.
- Despite being less familiar to many health researchers, FDR control is a valuable tool for enhancing statistical analysis.
- Adoption of FDR control can lead to more nuanced and powerful interpretations of research findings.
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