Geodesic
Assessing diagnostic accuracy of quantitative data in biomedical studies using descriptive statistics and standardized mean difference
Matematičeskaâ biologiâ i bioinformatika, Tome 15 (2020) no. 2, pp. 416-428.

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ROC analysis is the most used method for analyzing the diagnostic accuracy of quantitative data in biomedical research. ROC analysis generates a curve describing the frequencies of true positive and false positive results for different degrees of the analyzed variable. However, in many publications devoted to the application of quantitative diagnostic methods, this analysis is not carried out: researchers report only analysis of statistical significance for the groups difference. In meta-analyses, the estimated parameter is the effect size expressed through standardized mean difference. The article describes the approach, which allows performing ROC analysis using cumulative normal distribution functions for studied and controlling groups. The proposed approach can be used to evaluate the diagnostic accuracy of quantitative variables on the base of one of the sets of descriptive statistics (mean and standard deviation, or median and quartiles) or the value of standardized mean difference. Examples of application of the proposed approach on model data, on data from literature sources, as well as on the authors' own observations are given as an example of assessment of diagnostic accuracy of quantitative variables analyzed in the microcirculation studies in various diseases. The results presented in the article can be used by medical and biological specialists to assess the diagnostic accuracy of various quantitative variables without access to primary data. 
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A. A. Glazkov; D. A. Kulikov; P. A. Glazkova. Assessing diagnostic accuracy of quantitative data in biomedical studies using descriptive statistics and standardized mean difference. Matematičeskaâ biologiâ i bioinformatika, Tome 15 (2020) no. 2, pp. 416-428. http://geodesic.mathdoc.fr/item/MBB_2020_15_2_a1/

[1] I. Yu. Boiko, D. S. Anisimov, L. L. Smolyakova, M. A. Ryazanov, “Podkhod k otboru znachimykh priznakov pri reshenii biomeditsinskikh zadach binarnoi klassifikatsii dannykh s mikrochipov”, Matematicheskaya biologiya i bioinformatika, 15:1 (2020), 4–19 | DOI

[2] D. Alemayehu, K. H. Zou, “Applications of ROC analysis in medical research: recent developments and future directions”, Academic Radiology, 19:12 (2012), 1457–1464 | DOI

[3] C. T. Nakas, B. Reiser, “Editorial for the special issue of “Statistical Methods in Medical Research” on “Advanced ROC analysis””, Statistical Methods in Medical Research, 27:3 (2018), 649–650 | DOI | MR

[4] D. W. Hosmer, S. Lemeshow, R. X. Sturdivant, Applied Logistic Regression, John Wiley Sons, 2013, 528 pp. | MR | Zbl

[5] A. Fronek, M. Allison, “Noninvasive evaluation of endothelial activity in healthy and diseased individuals”, Vascular and Endovascular Surgery, 48:2 (2014), 134–138 | DOI

[6] Z. Ovadia-Blechman, I. Avrahami, E. Weizman-Shammai, T. Sharir, M. Eldar, P. Chouraqui, “Peripheral microcirculatory hemodynamic changes in patients with myocardial ischemia”, Biomedicine Pharmacotherapy, 74 (2015), 83–88 | DOI

[7] D. A. Kulikov, A. A. Glazkov, Yu. A. Kovaleva, N. V. Balashova, A. V. Kulikov, “Perspektivy ispolzovaniya lazernoi dopplerovskoi floumetrii v otsenke kozhnoi mikrotsirkulyatsii krovi pri sakharnom diabete”, Sakharnyi diabet, 20:4 (2017), 279–285 | DOI

[8] G. V. Glass, “Integrating Findings: The Meta-Analysis of Research”, Review of Research in Education, 5:1 (1977), 351–379 | DOI

[9] J. Cohen, “A power primer”, Psychological Bulletin, 112:1 (1992), 155–159 | DOI

[10] T. Fawcett, “An introduction to ROC analysis”, Pattern Recognition Letters, 27:8 (2006), 861–874 | DOI | MR

[11] N. J. Perkins, E. F. Schisterman, “The inconsistency of “optimal” cut-points using two ROC based criteria”, American Journal of Epidemiology, 163:7 (2006), 670–675 | DOI

[12] X. Wan, W. Wang, J. Liu, T. Tong, “Estimating the sample mean and standard deviation from the sample size, median, range and/or interquartile range”, BMC Medical Research Methodology, 14:135 (2014), 1–13

[13] R Core Team. R: A Language and Environment for Statistical Computing, , Vienna, 2013 (accessed 09.12.2020) http://www.R-project.org/

[14] D. K. Lee, “Alternatives to P value: Confidence interval and effect size”, Korean Journal of Anesthesiology, 69:6 (2016), 555–562 | DOI

[15] D. Kulikov, A. Glazkov, A. Dreval, Y. Kovaleva, D. Rogatkin, A. Kulikov, A. Molochkov, “Approaches to improve the predictive value of laser Doppler flowmetry in detection of microcirculation disorders in diabetes mellitus”, Clinical Hemorheology and Microcirculation, 70:2 (2018), 173–179 | DOI

[16] P. A. Glazkova, S. A. Terpigorev, D. A. Kulikov, N. A. Ivanova, A. A. Glazkov, “Puti povysheniya diagnosticheskoi znachimosti metoda lazernoi dopplerovskoi floumetrii pri otsenke kozhnoi mikrotsirkulyatsii u patsientov s arterialnoi gipertenziei”, Arterialnaya gipertenziya, 25:1 (2019), 74–83 | DOI | MR

[17] A. Cupisti, M. Rossi, S. Placidi, A. Fabbri, E. Morelli, G. Vagheggini, M. Meola, G. Barsotti, “Responses of the Skin Microcirculation to Acetylcholine in Patients with Essential Hypertension and in Normotensive Patients with Chronic Renal Failure”, Nephron, 85:2 (2000), 114–119 | DOI

[18] X. Robin, N. Turck, A. Hainard, N. Tiberti, F. Lisacek, J. C. Sanchez, M. Müller, “pROC: an open-source package for R and S+ to analyze and compare ROC curves”, BMC Bioinformatics, 12:1 (2011), 1–8 | DOI

[19] PubMed Database, (accessed 09.12.2020) https://pubmed.ncbi.nlm.nih.gov/

[20] S. S. Sawilowsky, “New effect size rules of thumb”, Journal of Modern Applied Statistical Methods, 8:2 (2009), 597–599 | DOI | MR

[21] D. Fuchs, P. P. Dupon, L. A. Schaap, R. Draijer, “The association between diabetes and dermal microvascular dysfunction non-invasively assessed by laser Doppler with local thermal hyperemia: a systematic review with meta-analysis”, Cardiovascular Diabetology, 16:11 (2017), 1–12 | DOI