False discovery rate control is a recommended alternative to Bonferroni-type adjustments in health studies.
OBJECTIVE
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.


METHODS
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.


CONCLUSIONS
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.
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. Electronic address: mg@bu.edu.
Mark E
ME
Glickman
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.
Sowmya R
SR
Rao
Center for Health care Organization and Implementation Research, Bedford VA Medical Center, 200 Springs Road (152), Bedford, MA 01730, USA.
Mark R
MR
Schultz
D035843:Biomedical Research / Q000379:methods* / Q000592:standards*; D056808:Biostatistics / Q000379:methods*; D003627:Data Interpretation, Statistical; D005189:False Positive Reactions; D006801:Humans; D016032:Randomized Controlled Trials as Topic / Q000379:methods / Q000523:psychology
FWER; False discovery rate; False positive rate; Multiple tests; P-value; Study-wide error rate