Data distribution patterns were tested by the Kolmogorov-Smirnov test. Normally distributed data was expressed as mean ± SD, and non-normal distributed data was expressed as mean and interquartile range (IQR). Categorical and ordinal variables were presented as numbers and ratios. Differences between the two groups were analysed with the unpaired t-test and Mann-Whitney U test. Correlations between HIF-1α and CD-133 concentrations were analysed by Spearman correlation analysis. Correlation coefficient rs rs = 0.70-0.89 was considered to be strong correlation, and rs = 0.40-0.69 moderate correlation (21 (link)). Categorical variables were expressed as frequencies and percentages and compared with Chi square test. To determine the HIF-1α and CD133 clinical usefulness in the studied group and assess the performance of the biomarker in distinguishing lymph node involvement positive and lymph node involvement negative, the marker values were reviewed by a receiver operating characteristic (ROC) analysis. The area under the ROC curve (AUC) serves as an overall measure of a biomarker/diagnostic test’s accuracy. The values P < 0.05 were considered statistically significant. Software packages SPSS version 18 (SPSS Inc., Chicago, USA) and MedCalc v.15.0 (MedCalc Software, Ostend, Belgium) were used for statistical analyses.
Medcalc v 15
Manufactured by MedCalc
Sourced in Belgium, United States
MedCalc V.15 is a software application designed for statistical analysis of medical data. It provides a range of functions for data management, statistical calculations, and graphical representation of results.
Automatically generated - may contain errors
16 protocols using medcalc v 15
1
Biomarker Analysis of Lymph Node Involvement
2
Evaluating Biomarkers for Venous Thromboembolism
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The statistical analysis was performed by using SPSS V.25.0 software and Medcalc V.15.0 software. The continuous variables were summarized using mean ± standard deviation (SD), categorical variables were summarized using frequency (percentage). Normality of data was tested by the Kolmogorov-Smirnov test. The mean values of D-dimer, PAI-1, TAT, and F1 + 2 plasma levels of time point were calculated for patients with and without VTE (VTE + group vs VTE– group). Between-group comparisons were performed using the Student t test. For dichotomous variables, the chi-square test was used. Results were deemed significant with p <0.05. For evaluation of plasma D-dimer, PAI-1, TAT, and F1 + 2 level discrimination points, a receiver–operating characteristic curve (ROC) analysis was conducted and the cutoff value was determined according to the maximization of the Youden index (J). Sensitivity, specificity, positive and negative predictive values (PPV and NPV), and positive and negative likelihood ratios (LR + and LR–) were also calculated. Pairwise comparison of ROC curves was also performed to evaluate the diagnostic value of those markers.
3
Diagnostic Efficacy of Thyroid Nodule Evaluation
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All statistical analyses were performed using SPSS v19.0 (SPSS Inc, Chicago, IL) and MedCalc v15.0 (MedCalc Software, Ostend, Belgium). If the variables were quantitative and normal, the mean ± standard deviation (SD) was used for statistical description, and the median with interquartile range (IQR) was used for the non-normal variables. Counting data is presented as numbers and percentage. The normal distribution data were compared between groups using an independent sample t-test. For non-normal distribution data, differences were analyzed using a Mann-Whitney U test. Chi-square test or McNemar’s test was used to compare the differences between groups for counting data. Linear-by-Linear Association test was used to analyze the linear trend of the malignant risk of CITNs. The ROC curve was drawn according to the histological results as the reference standard to find the best diagnostic cutoff value for the two TI-RADS classifications. In addition, the area under curve (AUC) of ROC curve was used to compare the diagnostic efficacy between S-Detect and the two TI-RADS classifications by Delong test (29 (link)). P values less than 0.05 were considered as statistically significant.
4
Atherosclerosis Markers and Left Ventricular Hypertrophy
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Statistical analysis was performed using SPSS 16.0 software (SPSS Inc., Chicago, IL) and MedCalc v.15.0 software (MedCalc. Software bvba, Ostend, Belgium). Quantitative data were presented as the mean ±SD
5
Predicting Therapeutic Response Using IVIM-DWI and GLCM
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All statistical analyses were done through SPSS version 22.0 (SPSS Inc, Chicago, IL) or MedCalc v15.0 software (MedCalc Software bvba, Ostend, Belgium). A 2-tailed test pattern was used in all statistical analyses with the level of statistical significance determined as P < .05. Categorical variables (gender, Tumor-Node-Metastasis [TNM] stage, and pathologic grade) were presented as frequency, and were compared using the Chi-squared test. Continuous variables (age, tumor volume, and the values of IVIM-DWI parameters and GLCM features) were expressed as mean ± standard deviation. The nonparametric Mann–Whitney U test was used in univariate analysis to explore the possible differences in continuous variables between the residue and nonresidue groups. Subsequently, multivariate logistic regression analysis (forward stepwise, LR; probability for stepwise entry, 0.05; removal, 0.1) was performed to identify the independent prognostic factors, using the indicators with statistical significance (P < .05) in univariate analysis as input variables. The discrimination power of the individual GLCM features and multivariate regression model for predicting the therapeutic response was determined with receiver operating characteristic (ROC) curve analysis.
6
Biomarker Analysis of Lymph Node Involvement
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Data distribution patterns were tested by the Kolmogorov-Smirnov test. Normally distributed data was expressed as mean ± SD, and non-normal distributed data was expressed as mean and interquartile range (IQR). Categorical and ordinal variables were presented as numbers and ratios. Differences between the two groups were analysed with the unpaired t-test and Mann-Whitney U test. Correlations between HIF-1α and CD-133 concentrations were analysed by Spearman correlation analysis. Correlation coefficient rs rs = 0.70-0.89 was considered to be strong correlation, and rs = 0.40-0.69 moderate correlation (21 (link)). Categorical variables were expressed as frequencies and percentages and compared with Chi square test. To determine the HIF-1α and CD133 clinical usefulness in the studied group and assess the performance of the biomarker in distinguishing lymph node involvement positive and lymph node involvement negative, the marker values were reviewed by a receiver operating characteristic (ROC) analysis. The area under the ROC curve (AUC) serves as an overall measure of a biomarker/diagnostic test’s accuracy. The values P < 0.05 were considered statistically significant. Software packages SPSS version 18 (SPSS Inc., Chicago, USA) and MedCalc v.15.0 (MedCalc Software, Ostend, Belgium) were used for statistical analyses.
7
Metabolite Profiling and Statistical Analysis
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The chi-square test was used to analyze categorical data (sex). All continuous variables such as age, BMI and metabolite concentrations, were analyzed using Student’s two-tailed t-test or one-way ANOVA followed by the Bonferroni post hoc test. All continuous variables were expressed as means ± standard errors of the mean. All analyses were performed with MedCalc v. 15.2.1 (MedCalc Software, Mariakerke, Belgium). A p-value of less than 0.05 was considered statistically significant. Heat maps of the metabolites were obtained using MetaboAnalyst 3.0 (http://www.metaboanalyst.ca/ )27 (link). This web server is designed to permit comprehensive metabolomic data analysis, visualization, and interpretation.
8
Statistical Analysis of Population Data
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Frequencies and percentages, medians, and interquartile range (IQR) were used to describe descriptive statistics. Kolmogorov–Smirnov test was used to determine whether data were normally distributed. Chi-square test was used for analyzing differences between rural and urban populations. We conducted analyses using MedCalc v 15.2.1 (MedCalc Software bvba, Ostend, Belgium). Statistical significance was set at p < 0.05.
9
Evaluation of CXCL13+ CD8+ T Cells
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Continuous variables were analyzed with Mann-Whitney test as appropriate. The relationship between CXCL13+CD8+T cell density and patient characteristics was evaluated by χ2 test. The correlation analyses were evaluated with non-parametric (Mann-Whitney U test and Spearman’s test) tests. Wilcoxon matched-pairs signed rank test was used for non-parametric pairing t-tests. Kruskal-Wallis test was applied to detect the association between CXCL13+CD8+T cells infiltration level and tumor stage. Results were shown in mean±SD. Kaplan-Meier method and Log rank test were applied to demonstrate survival curves between different groups in the cohort. Multivariate analyses of cox regression model were applied to estimate HRs and 95% CIs. All statistical analyses were performed with SPSS V.19.0 (IBM), R software V.4.0.2 (R Foundation for Statistical Computing, http://www.r-project.org/ ), Graphpad V.8.0 and Medcalc V.15. The heatmap was performed with use of https://software.broadinstitute.org/morpheus/ . All of these analyses were performed at the statistically significant level of 5% (p<0.05) and two tailed.
10
Echocardiographic Evaluation of Atrial Fibrillation
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Continuous variables were expressed as mean±SD. Results of values not normally distributed were presented as medians with IQR. Nominal values were expressed as numbers and percentages. Group comparisons were made using non-parametric Kruskal-Wallis test for continuous variables and Pearson χ2 test for categorical variables, using Mann-Whitney test and Fisher exact test, respectively, for multiple comparisons. Relationships between variables were assessed using Spearman correlation analysis and expressed by R. Comparison between receiver operator characteristic (ROC) curves was made using DeLong test. The best cut-off value was defined as the point with the highest sum of sensitivity and specificity. The intraobserver and interobserver variability of measurements was analysed using the intraclass correlation coefficient from 20 randomly selected patients and from 10 patients with atrial fibrillation reanalysed by two observers blinded to other echocardiographic results. Differences were considered statistically significant for p values of <0.05. All analyses were performed using standard statistical software, SPSS V.20 and MedCalc V.15 (MedCalc, Mariakerke, Belgium).
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