Geodesic
Determining subgroups of significant correlation in analyzing relation between NR2 antibodies and factor VIII in acute neurological diseases
Matematičeskaâ biologiâ i bioinformatika, Tome 16 (2021) no. 1, pp. 29-38.

Voir la notice de l'article provenant de la source Math-Net.Ru

The paper considers a new method for finding correlations distorted by the influence of a third factor. In other words, the method is designed to search for effects consisted in the existence a significant correlation between two variables in a group of observations received from the main sample by removal of the observations with extremal values of a third factor. Testing for such effects should include both an assessment of the statistical significance of the correlation in the subgroup and the significance of the influence of the third factor. Potentially, this can be done using the previously developed method of valid conditional linear regularities (VCLR). The statistics used in the VCLR method is the maximum of the functional, which depends on the correlation coefficients in the subgroups identified using the threshold for the third factor and the sizes of these subgroups. The disadvantage of this method is that it cannot be used if the maximum of the functional corresponds to a threshold value that cuts off a small group. This drawback did not allow to adequately assess the significance of the effect associated with the existence of a significant negative correlation between the serum levels of antibodies to factor VIII and NR2 in the group of patients with ischemic stroke and transient ischemic attack after excluding patients with abnormally high levels of vascular endothelial growth factor. An alternative method was proposed, which is based on a permutation test. At that the statistics of the test is a minimum p-value from those characterizing the correlation coefficients calculated using the normal approximation of the Fisher's z-transform corresponding to all possible threshold values for the third factor. The use of the new criterion made it possible to adequately assess the significance of the observed effect. 
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     author = {A. N. Mazilina and O. V. Senko and O. S. Brusov and A. A. Dokukin and M. S. Kodryan and A. V. Kuznetsova and L. L. Klimenko},
     title = {Determining subgroups of significant correlation in analyzing relation between {NR2} antibodies and factor {VIII} in acute neurological diseases},
     journal = {Matemati\v{c}eska\^a biologi\^a i bioinformatika},
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A. N. Mazilina; O. V. Senko; O. S. Brusov; A. A. Dokukin; M. S. Kodryan; A. V. Kuznetsova; L. L. Klimenko. Determining subgroups of significant correlation in analyzing relation between NR2 antibodies and factor VIII in acute neurological diseases. Matematičeskaâ biologiâ i bioinformatika, Tome 16 (2021) no. 1, pp. 29-38. http://geodesic.mathdoc.fr/item/MBB_2021_16_1_a2/

[1] Kuznetsova A.V., Kostomarova I.V., Sen'ko O.V., “Modification of the method of optimal valid partitioning for comparison of patterns related to the occurence of ischemic stroke in two groups of patients”, Pattern Recognition and Image Analysis, 24 (2014), 114–123 | DOI

[2] Kodryan M.S., Kuznetsova A.V., Klimenko L.L., Mazilina A.N., Baskakov I.V., Senko O.V., “Nonparametric Method for Estimation of Controlled Correlations in Studies of VEGF-Hypoxia Relationship”, Int. J. Clin. Biostat. Biom., 24:6 (2020) | DOI

[3] Senko O.V., Kodryan M.S., Kuznecova A.V., Klimenko L.L., Deev A.I., Baskakov I.S., Mazilina A.N., “Optimal Partitioning Method for Evaluating of Effect of Hemoglobin Oxygenation Levels of Vessel Endothelial Growth Factor”, Mathematical Biology and Bioinformatics, 13:2 (2018), 563–590 (in Russ.) | DOI

[4] Anderson M.J., Robinson J., “Permutation tests for linear models”, Aust. N. Z. J. Stat., 43 (2001), 75–88 | DOI | MR | Zbl

[5] Pesarin F., Salmaso L., Permutation tests for complex data: Theory, Applications and Software, John Wiley and Sons, Ltd, New Jersey, 2010, 448 pp. | DOI | MR

[6] Weissman J.D., Khunteev G.A., Heath R., Dambinova S.A., “NR2 antibodies: risk assessment of transient ischemic attack (TIA)/stroke in patients with history of isolated and multiple cerebrovascular events”, J. Neurol. Sci., 300:1-2 (2011), 97–102 | DOI

[7] Dambinova S.A., Bettermann K., Glynn T., , Tews M., Olson D., Weissman J.D., Sowell R.L., “Diagnostic potential of the NMDA receptor peptide assay for acute ischemic stroke”, PLoS One, 7:7 (2012) | DOI

[8] Dambinova S.A., Aliev K.T., Bondarenko E.V., Ponomarev G.V., Skoromec A.A., Skoromec A.P., Skoromec T.A., Smolko D.G., SHumilina M.V., “The biomarkers of cerebral ischemia as a new method for the validation of the efficacy of cytoprotective therapy”, S.S. Korsakov Journal of Neurology and Psychiatry, 5 (2017), 62-67 (in Russ.) | DOI

[9] Chih-Yu Kuo, Chun-Hsien Lin, Ya-Wen Kuo, Yen-Chu Huang, Huan-Lin Hsu, Ya-Hui Lin, Chih-Ying Wu, Ying-Chih Huang, Meng Lee, Hsin-Ta Yang, Chia-Yu Hsu, Yi-Ting Pan, Jiann-Der Lee, “Factor VIII levels are associated with ischemic stroke, stroke subtypes and neurological worsening”, Curr. Neurovasc. Res., 1:12 (2015), 85–90 | DOI

[10] Samai A.A., Boehme A.K., Shaban A., George A.J., Dowell L., Monlezun D.J., Leissinger C., Schluter L., El Khoury R., Martin-Schild S., “A Model for Predicting Persistent Elevation of Factor VIII among Patients with Acute Ischemic Stroke”, Journal of Stroke and Cerebrovascular Diseases, 25:2 (2016), 428–435 | DOI

[11] Prokhorov A.V., “Partial correlation coefficient”, Encyclopedia of Mathematics, 7, Springer, 1991 | MR

[12] Fisher R.A., “Frequency distribution of the values of the correlation coefficient in samples of an indefinitely large population”, Biometrika, 4:10 (1915), 507–521 | DOI

[13] Duncan E., Collecutt M., Street A., “Nijmegen-Bethesda assay to measure factor VIII inhibitors”, Methods Mol. Biol., 992 (2013), 321–333 | DOI

[14] Lassila R., “Management of coagulation factor VIII (FVIII) inhibitors”, Thromb. Res., 181:1 (2019), S60–S61 | DOI