Journal of Pathology Informatics Journal of Pathology Informatics
Contact us | Home | Login   |  Users Online: 252  Print this pageEmail this pageSmall font sizeDefault font sizeIncrease font size 




 
Table of Contents    
TECHNICAL NOTE
J Pathol Inform 2011,  2:52

An open-source software program for performing Bonferroni and related corrections for multiple comparisons


1 Faculty of Medicine, Bachelor of Health Sciences Program, Room G503, O'Brien Centre for the BHSc, 3330 Hospital Drive N.W. Calgary, Alberta T2N 4N1, 2, Canada
2 Departments of Pathology and Laboratory Medicine, University of Calgary and Calgary Laboratory Services, C414, Diagnostic and Scientific Centre, 9, 3535 Research Road NW, Calgary AB Canada T2L 2K8, Canada

Date of Submission07-Sep-2011
Date of Acceptance18-Nov-2011
Date of Web Publication26-Dec-2011

Correspondence Address:
Christopher Naugler
Departments of Pathology and Laboratory Medicine, University of Calgary and Calgary Laboratory Services, C414, Diagnostic and Scientific Centre, 9, 3535 Research Road NW, Calgary AB Canada T2L 2K8
Canada
Login to access the Email id

Source of Support: None, Conflict of Interest: None


DOI: 10.4103/2153-3539.91130

Rights and Permissions
   Abstract 

Increased type I error resulting from multiple statistical comparisons remains a common problem in the scientific literature. This may result in the reporting and promulgation of spurious findings. One approach to this problem is to correct groups of P-values for "family-wide significance" using a Bonferroni correction or the less conservative Bonferroni-Holm correction or to correct for the "false discovery rate" with a Benjamini-Hochberg correction. Although several solutions are available for performing this correction through commercially available software there are no widely available easy to use open source programs to perform these calculations. In this paper we present an open source program written in Python 3.2 that performs calculations for standard Bonferroni, Bonferroni-Holm and Benjamini-Hochberg corrections.

Keywords: Bonferroni correction, software program, type I error


How to cite this article:
Lesack K, Naugler C. An open-source software program for performing Bonferroni and related corrections for multiple comparisons. J Pathol Inform 2011;2:52

How to cite this URL:
Lesack K, Naugler C. An open-source software program for performing Bonferroni and related corrections for multiple comparisons. J Pathol Inform [serial online] 2011 [cited 2019 May 19];2:52. Available from: http://www.jpathinformatics.org/text.asp?2011/2/1/52/91130


   Background Top


When multiple hypotheses are tested in a single experiment, the risk of type I error is increased and with it the risk of promulgating spurious "significant" findings. [1],[2],[3] The likelihood of obtaining a false positive result increases proportional to the number of tests performed. For example, the probability of obtaining at least one false positive result when performing 10 tests is given by



where P(A) is the confidence level of the test.

Although the problems associated with multiple testing are well known, numerous studies still fail to correct their reported P-values. For instance, Bennett et al. found that only between 60% and 74% of the neuroimaging articles published in several major journals corrected for multiple comparisons. [4] Similarly, a study performed by Austin et al. also demonstrated that the failure to account for multiple testing resulted in statistically significant, yet implausible results. [5] In both cases the results were no longer significant after correcting for multiple testing.

The lack of attention paid to this problem in the pathology literature stands in stark contrast to its recognition in other fields such as ecology where there has been intense interest for over two decades since the seminal publication by Rice. [6] That being said, even within the field of ecology this topic still engenders debate. [7] A systematic exploration of this problem in the pathology literature has not been undertaken; however we have previously reported on a convenience sample of 800 publications from the pathology literature in 2003, of which 37 presented multiple comparisons. Twenty one of these 37 did not attempt to control for increased type I error due to multiple comparisons. [8]

One means of reducing the type I error from multiple testing is the Bonferroni correction, which controls the family-wise error rate (FWER). The FWER is the probability of type I error among the entire set of hypotheses.

The Bonferroni correction is calculated as follows:



where n is the number of hypotheses tested. There is a lack of consensus as to what actually represents a "family" of statistical tests; however it has been suggested that if it is appropriate to place multiple P-values in the same table, it may be appropriate to correct all values in that table for multiple comparisons. [6]

Because the Bonferroni correction is conservative with regard to statistical power, other methods of correcting for multiple testing have been developed. Another method that controls for the FWER is the Bonferroni-Holm correction. [9] The Bonferroni-Holm correction is calculated as follows:



where n is the number of hypotheses tested, and k is the ordered rank of the uncorrected P-values (from smallest P-value to largest P-value).

Rather than controlling for the probability of one or more type I errors in the entire experiment, some of the more recent approaches to the multiple testing problem have focused on controlling the false discovery rate (FDR) in the experiment. By controlling the proportion of type I errors, this has the advantage of further increasing the statistical power of the algorithm, and is especially suitable when conducting numerous hypothesis tests. [10],[11] The Benjamini-Hochberg method [12] is a commonly used way to control the FDR of an experiment. It is calculated as follows:



where n is the number of hypotheses tested, and k is the rank of the uncorrected P value.

Several commercial statistical software packages are capable of performing one or more of these corrections as well as at least one open-source program (GNU R); however the cost of the commercial packages, and the learning curves involved, may discourage researchers from using these programs. Online tools are also available (e.g., http://www.quantitativeskills.com/sisa/calculations/bonfer.htm) but are limited in scope and available options and rely on continued access to the publisher's website.

"Bonferroni Calculator" software

Using the open-source programming language Python v 3.2, we developed a program capable of performing Bonferroni, Bonferroni-Holm, and Benjamini-Hochberg corrections for any number of P-values. The user is prompted for a set of P-values and the desired significance (alpha) level. From the main menu the user may choose to display the results of the desired correction to the screen, or to export the corrected P values to the hard disk (text and csv file types). The source code is available free as a supplementary file to this article (which may serve as a literature reference for the program). A copy of the source code may also be obtained by email from the corresponding author. The program requires the free programming language Python 3.2 which is capable of running on Microsoft Windows, MAC OS, and Linux/Unix operating systems. It may be downloaded from http://www.python.org/getit/releases/3.2/.

The program is available for free by emailing the senior author at christopher.naugler@cls.ab.ca. Detailed instructions and a FAQ are available at https://sites.google.com/site/christophernaugler/. To use the Bonferroni Calculator software, place the files "Bonferroni Calculator.py" and "Lesack and Naugler.txt" in a folder on your hard drive. In windows, the program will run from the command line by double clicking on the "Bonferroni Calculator.py" icon; however the preferred method is to right click on the icon and select "Edit with IDLE" from the dropdown list. Press F5 to run the software, and then maximize the size of the window. Follow the instructions on the screen. If the option is selected to save the results to files, these will be found in the same folder as the "Bonferroni Calculator.py" icon. The program is also available from the authors as a stand-alone executable file.

 
   References Top

1.Koch G, Gansky M. Statistical considerations for multiplicity in confirmatory protocols. Drug Inf J 1996;30:523-33.  Back to cited text no. 1
    
2.Bender R, Lange S. Adjusting for multiple testing-when and how? J Clin Epidemiol 2001;54:343-9.  Back to cited text no. 2
    
3.Karr A, Young SS. Deming, data and observational studies. Significance 2011;8:116-120.  Back to cited text no. 3
    
4.Bennett CM, Baird AA, Miller MB, Wolford GL. Neural correlates of interspecies perspective taking in the post-mortem Atlantic Salmon: an argument for multiple comparisons correction. J Serendipitous Unexpected Results 2010;1:1-5.  Back to cited text no. 4
    
5.Austin PC, Mamdani MM, Juurlink DN, Hux JE. Testing multiple statistical hypotheses resulted in spurious associations: a study of astrological signs and health. J Clin Epidemiol 2006;59:964-9.  Back to cited text no. 5
    
6.Rice WR. Analyzing tables of statistical tests. Evolution 1989;43:223-5.  Back to cited text no. 6
    
7.Nakagawa S. A farewell to Bonferroni: the problems of low statistical power and publication bias. Behav Ecol 2004;15:1044-5.  Back to cited text no. 7
    
8.Zheng Z, Naugler C. Type I error in pathology papers, prevalence and effect on publication citations. Poster Presentation, Canadian Association of Pathologists Annual Scientific Meeting, Montreal, PQ, Jul 11-15 2010.  Back to cited text no. 8
    
9.Holm S. A simple sequentially rejective multiple test procedure. Scand J Stat 1979;6:65-70.  Back to cited text no. 9
    
10.García LV. Escaping the Bonferroni iron claw in ecological studies. Oikos 2004;105:657-63.  Back to cited text no. 10
    
11.Wit E, McClure J. Statistics for microarrays: Design, Analysis, and Inference. 1 st ed. Hoboken, New Jersey: John Wiley and Sons; 2004. p.195.  Back to cited text no. 11
    
12.Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Series B 1995;57:289-300.  Back to cited text no. 12
    



This article has been cited by
1 FleQ regulates both the type VI secretion system and flagella in Pseudomonas putida
Yuzhou Wang,Ye Li,Jianli Wang,Xiaoyuan Wang
Biotechnology and Applied Biochemistry. 2017;
[Pubmed] | [DOI]
2 Physiological and parasitological implications of living in a city: the case of the white-footed tamarin (Saguinus leucopus)
Iván Darío Soto-Calderón,Yuliet Andrea Acevedo-Garcés,Jóhnatan Álvarez-Cardona,Carolina Hernández-Castro,Gisela María García-Montoya
American Journal of Primatology. 2016;
[Pubmed] | [DOI]
3 Gene expression signatures, pathways and networks in carotid atherosclerosis
L. Perisic,S. Aldi,Y. Sun,L. Folkersen,A. Razuvaev,J. Roy,M. Lengquist,S. Åkesson,C. E. Wheelock,L. Maegdefessel,A. Gabrielsen,J. Odeberg,G. K. Hansson,G. Paulsson-Berne,U. Hedin
Journal of Internal Medicine. 2016; 279(3): 293
[Pubmed] | [DOI]
4 Statistics Commentary Series
David L. Streiner
Journal of Clinical Psychopharmacology. 2016; 36(1): 5
[Pubmed] | [DOI]
5 Linking GABA and glutamate levels to cognitive skill acquisition during development
Kathrin Cohen Kadosh,Beatrix Krause,Andrew J. King,Jamie Near,Roi Cohen Kadosh
Human Brain Mapping. 2015; 36(11): 4334
[Pubmed] | [DOI]
6 Customizing Laboratory Information Systems
Peter Gershkovich,John H. Sinard
Advances In Anatomic Pathology. 2015; 22(5): 323
[Pubmed] | [DOI]
7 Integrated Metabolomic and Proteomic Analysis Reveals Systemic Responses ofRubrivivax benzoatilyticusJA2 to Aniline Stress
Md Mujahid,M Lakshmi Prasuna,Ch Sasikala,Ch Venkata Ramana
Journal of Proteome Research. 2015; 14(2): 711
[Pubmed] | [DOI]
8 The impact of ginsenosides on cognitive deficits in experimental animal studies of Alzheimer’s disease: a systematic review
Chenxia Sheng,Weijun Peng,Zi-an Xia,Yang Wang,Zeqi Chen,Nanxiang Su,Zhe Wang
BMC Complementary and Alternative Medicine. 2015; 15(1)
[Pubmed] | [DOI]
9 Population Genetic Structure of Southern Flounder Inferred from Multilocus DNA Profiles
Verena H. Wang,Michael A. McCartney,Frederick S. Scharf
Marine and Coastal Fisheries. 2015; 7(1): 220
[Pubmed] | [DOI]
10 Treatment and posttreatment effects induced by the Forsus appliance:A controlled clinical study
Giorgio Cacciatore,Luis Tomas Huanca Ghislanzoni,Lisa Alvetro,Veronica Giuntini,Lorenzo Franchi
The Angle Orthodontist. 2014; 84(6): 1010
[Pubmed] | [DOI]
11 A novel compression garment with adhesive silicone stripes improves repeated sprint performance – a multi-experimental approach on the underlying mechanisms
Dennis-Peter Born,Hans-Christer Holmberg,Florian Goernert,Billy Sperlich
BMC Sports Science, Medicine and Rehabilitation. 2014; 6(1): 21
[Pubmed] | [DOI]
12 Short-term effects of a modified Alt-RAMEC protocol for early treatment of Class III malocclusion: a controlled study
C. Masucci,L. Franchi,V. Giuntini,E. Defraia
Orthodontics & Craniofacial Research. 2014; 17(4): 259
[Pubmed] | [DOI]
13 Anterior-posterior cerebral blood volume gradient in human subiculum
Pratik Talati,Swati Rane,Samet Kose,John Gore,Stephan Heckers
Hippocampus. 2014; : n/a
[Pubmed] | [DOI]
14 Characteristics of cognitive deficits and writing skills of Polish adults with developmental dyslexia
Katarzyna Maria Bogdanowicz,Marta Lockiewicz,Marta Bogdanowicz,Maria Pachalska
International Journal of Psychophysiology. 2013;
[Pubmed] | [DOI]
15 Sleepiness and nocturnal hypoxemia in Peruvian men with obstructive sleep apnea
Charles Huamaní,Jorge Rey de Castro,Edward Mezones-Holguín
Sleep and Breathing. 2013;
[Pubmed] | [DOI]
16 Investigation of genetic risk factors for chronic adult diseases for association with preterm birth
Nadia Falah,Jude McElroy,Victoria Snegovskikh,Charles J. Lockwood,Errol Norwitz,Jeffey C. Murray,Edward Kuczynski,Ramkumar Menon,Kari Teramo,Louis J. Muglia,Thomas Morgan
Human Genetics. 2013; 132(1): 57
[Pubmed] | [DOI]



 

 
Top
  

    

 
  Search
 
   Browse articles
  
    Similar in PUBMED
   Search Pubmed for
   Search in Google Scholar for
 Related articles
    Access Statistics
    Email Alert *
    Add to My List *
* Registration required (free)  

 
  In this article
    Abstract
   Background
    References

 Article Access Statistics
    Viewed6288    
    Printed171    
    Emailed0    
    PDF Downloaded864    
    Comments [Add]    
    Cited by others 16    

Recommend this journal