- Email: [email protected]
Statistical support for Sema3A and multiple sclerosis
Gene 626 (2017) 87–88
Contents lists available at ScienceDirect
Gene journal homepage: www.elsevier.com/locate/gene
Letter to the Editor Statistical support for Sema3A and multiple sclerosis
MARK
DearWe Editor read with interest the letter of Farrokhi (Farrokhi, 2017) in which the statistical analysis of the article by Rezaeepoor et al. (Rezaeepoor et al., 2017) was questioned. The following issues were raised in this letter: 1) ANOVA for comparing three study groups would lead to invalid conclusions because the two MS (multiple sclerosis) patients as relapsing and remitting groups are not independent. Thus, the paired sample t-test should be considered for analysis showing differences between these two groups. Response: It is basically true that the paired sample t-test is more powerful than the independent sample t-test when two related samples have meaningful relationship (i.e. dependency). However, the statistical power improvement is only granted when the two sets of measurements are correlated with each other (McDonald, 2009). It was shown in the literature that if there is a little between and within-subject correlation, the paired and unpaired tests give similar results (Donald, 2012; Zimmerman et al., 1993). Having analyzed the data available in Table 2 in the Rezaeepoor et al. (2017) paper, there is not a significant linear correlation between Sema3A in relapsing and remitting groups (r = 0.104, p-value = 0.712). Thus, independent-samples tests could also be used. Nevertheless, we also used paired sample t-test and no significant difference was found (pvalue = 0.698). This is identical to what was reported in our paper. Moreover, we also considered more general measures of dependency in addition to linear correlation. Dependency is a wide-sense measure that is not always detected by correlation (Mari and Kotz, 2001). Mutual information (MI) criteria, on the other hand, is a better indicator of dependency (Kinney and Atwal, 2014). Such measures were implemented in R and none of them showed reliable association between relapsing and remitting groups, indicating that the administration of immunosuppressive drugs did not significantly affect Sema3A expression. 2) Independent samples t-test should be used for comparison of Sema3A levels between healthy controls and relapsing or remitting groups. Response: Having identified the independence of the relapsing and remitting groups at the first place, the control group was also independent from the two patients groups. Thus, the application of ANOVA is justified. Moreover, it was shown in the literature that in a case of little between and within-subject correlations, it is possible to use ANOVA (Zimmerman et al., 1993). Moreover, it is well-known in the literature that repeating a statistical test increases the Type I error (e.g. running t-test three times, Type I error increases up to 0.14). Using ANOVA, such multiple errors are controlled so that the Type I error remains at 0.05. Thus, the statistical test that Rezaeepoor et al. (Rezaeepoor et al., 2017) have used is actually preferred to what was proposed by Farrokhi (Farrokhi, 2017). 3) To use EDSS scoring and MRI for relating the disease activity to Sema3A serum levels. Response: As it is written in the last paragraph of the Rezaeepoor et al. (Rezaeepoor et al., 2017) paper, this paper is only a preliminary study to investigate the role of Sema3A in MS which revealed a new aspect of immunopathogenesis of MS. Also, it is exactly noted that: “This suggests that Sema3A may be an effective marker for MS disease activity. Further large-scale studies are needed to confirm this notion”. Thus, it is not something claimed by the authors and it is only a suggestion for future studies. It was not reasonable for the authors to considerer this as one of their objectives at the first, because the possible correlation between Sema3A serum level and MS disease per se was still unclear. Declaration of interest The authors report no conflicts of interest. References Donald, W., 2012. Correcting two-sample z and t tests for correlation: an alternative to one-sample tests on difference scores. Psicológica 33, 391–418. Farrokhi, M., 2017. Sema3A and multiple sclerosis. Gene 615, 41. Kinney, J.B., Atwal, G.S., 2014. Equitability, mutual information, and the maximal information coefficient. Proc. Natl. Acad. Sci. 111, 3354–3359. Mari, D.D., Kotz, S., 2001. Correlation and Dependence. Imperial College Press, London, River Edge, NJ Distributed by World Scientific Pub. Co. McDonald, J.H., 2009. Handbook of Biological Statistics. Sparky House Publishing, Baltimore, MD. Rezaeepoor, M., Shapoori, S., Ganjalikhani-Hakemi, M., Etemadifar, M., Alsahebfosoul, F., Eskandari, N., Mansourian, M., 2017. Decreased expression of Sema3A, an immune modulator, in blood sample of multiple sclerosis patients. Gene 610, 59–63. Zimmerman, D.W., Williams, R.H., Zurabo, B.D., 1993. Effect of nonindependence of sample observations on some parametric and nonparametric statistical tests. Commun. Stat. Simul. Comput. 22, 779–789. http://dx.doi.org/10.1016/j.gene.2017.05.022 Received 18 April 2017; Received in revised form 4 May 2017; Accepted 9 May 2017 Available online 10 May 2017 0378-1119/ © 2017 Published by Elsevier B.V.
Gene 626 (2017) 87–88
Letter to the Editor
Mahsa Rezaeepoor, Shima Shapoori, Mazdak Ganjalikhani-hakemi,, Masoud Etemadifar, Fereshteh Alsahebfosoul, Nahid Eskandari, Marjan Mansourian Department of Immunology, Faculty of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran Multiple Sclerosis and Neuroimmunology Research Center, Isfahan University of Medical Sciences, Isfahan, Iran Department of Biostatistics and Epidemiology, Faculty of Medicine, Isfahan University of Medical Science, Isfahan, Iran E-mail address: [email protected]
⁎
Corresponding author.
88
Contents lists available at ScienceDirect
Gene journal homepage: www.elsevier.com/locate/gene
Letter to the Editor Statistical support for Sema3A and multiple sclerosis
MARK
DearWe Editor read with interest the letter of Farrokhi (Farrokhi, 2017) in which the statistical analysis of the article by Rezaeepoor et al. (Rezaeepoor et al., 2017) was questioned. The following issues were raised in this letter: 1) ANOVA for comparing three study groups would lead to invalid conclusions because the two MS (multiple sclerosis) patients as relapsing and remitting groups are not independent. Thus, the paired sample t-test should be considered for analysis showing differences between these two groups. Response: It is basically true that the paired sample t-test is more powerful than the independent sample t-test when two related samples have meaningful relationship (i.e. dependency). However, the statistical power improvement is only granted when the two sets of measurements are correlated with each other (McDonald, 2009). It was shown in the literature that if there is a little between and within-subject correlation, the paired and unpaired tests give similar results (Donald, 2012; Zimmerman et al., 1993). Having analyzed the data available in Table 2 in the Rezaeepoor et al. (2017) paper, there is not a significant linear correlation between Sema3A in relapsing and remitting groups (r = 0.104, p-value = 0.712). Thus, independent-samples tests could also be used. Nevertheless, we also used paired sample t-test and no significant difference was found (pvalue = 0.698). This is identical to what was reported in our paper. Moreover, we also considered more general measures of dependency in addition to linear correlation. Dependency is a wide-sense measure that is not always detected by correlation (Mari and Kotz, 2001). Mutual information (MI) criteria, on the other hand, is a better indicator of dependency (Kinney and Atwal, 2014). Such measures were implemented in R and none of them showed reliable association between relapsing and remitting groups, indicating that the administration of immunosuppressive drugs did not significantly affect Sema3A expression. 2) Independent samples t-test should be used for comparison of Sema3A levels between healthy controls and relapsing or remitting groups. Response: Having identified the independence of the relapsing and remitting groups at the first place, the control group was also independent from the two patients groups. Thus, the application of ANOVA is justified. Moreover, it was shown in the literature that in a case of little between and within-subject correlations, it is possible to use ANOVA (Zimmerman et al., 1993). Moreover, it is well-known in the literature that repeating a statistical test increases the Type I error (e.g. running t-test three times, Type I error increases up to 0.14). Using ANOVA, such multiple errors are controlled so that the Type I error remains at 0.05. Thus, the statistical test that Rezaeepoor et al. (Rezaeepoor et al., 2017) have used is actually preferred to what was proposed by Farrokhi (Farrokhi, 2017). 3) To use EDSS scoring and MRI for relating the disease activity to Sema3A serum levels. Response: As it is written in the last paragraph of the Rezaeepoor et al. (Rezaeepoor et al., 2017) paper, this paper is only a preliminary study to investigate the role of Sema3A in MS which revealed a new aspect of immunopathogenesis of MS. Also, it is exactly noted that: “This suggests that Sema3A may be an effective marker for MS disease activity. Further large-scale studies are needed to confirm this notion”. Thus, it is not something claimed by the authors and it is only a suggestion for future studies. It was not reasonable for the authors to considerer this as one of their objectives at the first, because the possible correlation between Sema3A serum level and MS disease per se was still unclear. Declaration of interest The authors report no conflicts of interest. References Donald, W., 2012. Correcting two-sample z and t tests for correlation: an alternative to one-sample tests on difference scores. Psicológica 33, 391–418. Farrokhi, M., 2017. Sema3A and multiple sclerosis. Gene 615, 41. Kinney, J.B., Atwal, G.S., 2014. Equitability, mutual information, and the maximal information coefficient. Proc. Natl. Acad. Sci. 111, 3354–3359. Mari, D.D., Kotz, S., 2001. Correlation and Dependence. Imperial College Press, London, River Edge, NJ Distributed by World Scientific Pub. Co. McDonald, J.H., 2009. Handbook of Biological Statistics. Sparky House Publishing, Baltimore, MD. Rezaeepoor, M., Shapoori, S., Ganjalikhani-Hakemi, M., Etemadifar, M., Alsahebfosoul, F., Eskandari, N., Mansourian, M., 2017. Decreased expression of Sema3A, an immune modulator, in blood sample of multiple sclerosis patients. Gene 610, 59–63. Zimmerman, D.W., Williams, R.H., Zurabo, B.D., 1993. Effect of nonindependence of sample observations on some parametric and nonparametric statistical tests. Commun. Stat. Simul. Comput. 22, 779–789. http://dx.doi.org/10.1016/j.gene.2017.05.022 Received 18 April 2017; Received in revised form 4 May 2017; Accepted 9 May 2017 Available online 10 May 2017 0378-1119/ © 2017 Published by Elsevier B.V.
Gene 626 (2017) 87–88
Letter to the Editor
Mahsa Rezaeepoor, Shima Shapoori, Mazdak Ganjalikhani-hakemi,, Masoud Etemadifar, Fereshteh Alsahebfosoul, Nahid Eskandari, Marjan Mansourian Department of Immunology, Faculty of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran Multiple Sclerosis and Neuroimmunology Research Center, Isfahan University of Medical Sciences, Isfahan, Iran Department of Biostatistics and Epidemiology, Faculty of Medicine, Isfahan University of Medical Science, Isfahan, Iran E-mail address: [email protected]
⁎
Corresponding author.
88