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Statistical software version 11

Manufactured by MedCalc
Sourced in Belgium

MedCalc is a statistical software package for Windows designed for medical research. Version 11.0 provides a comprehensive set of statistical tools for data management, data analysis, and presentation of results. The software is intended for use by medical researchers and professionals in the healthcare industry.

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10 protocols using statistical software version 11

1

Survival Analysis of Serum LDH Levels

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The independent-samples t test was used to calculate differences between serum LDH levels from the various patient groups. Receiver-operating characteristic (ROC) curve analysis was adopted to determine the most appropriate cut-off points of LDH for survival. The primary endpoint of this study was distant metastasis-free survival (DMFS) and the secondary endpoint was overall survival (OS). DMFS was defined as the time from treatment to the first observation of distant metastasis. OS was measured as the first date of treatment to the date of death. Patients still alive or without progression were censored at the date of the last contact. The Kaplan-Meier method and log-rank test were adopted to calculate and compare the DMFS and OS rates. Univariate and multivariate analyses were carried out using the Cox proportional hazards model. All statistical tests were two-sided, and p<0.05 was considered statistically significant. Statistical analyses were performed using MedCalc statistical software version 11.0 (MedCalc Software, Mariakerke, Belgium) and the Statistical Package for Social Sciences version 19.0 (SPSS Inc., Chicago, IL, USA).
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2

Ocular Lens Parameters in Hyperopia and Myopia

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All statistical analyses were performed using MedCalc Statistical Software version 11.0 (MedCalc Software Inc., Mariakerke, Belgium). Statistical significance was set at P < 0.05. The distribution of all datasets was analyzed for normality using Kolmogorov–Smirnov tests. A paired t-test was applied to compare the ocular lens parameters obtained pre- and post-cycloplegia in the two groups. An independent sample t-test was used to compare ocular lens parameters between hyperopic and myopic eyes.
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3

ADAM17 Prognostic Survival Analysis

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Univariate survival analysis for the comparison of survival times was conducted using the Kaplan-Meier method, and the log-rank test was applied to analyze the significance of difference between survival times of patients and their clinicopathological features, as well as between survival times and ADAM9 or ADAM17 expression. Multivariate survival analysis was performed for identifying significant variables associated with survival times using the Cox regression proportional hazards model. In addition, the risk scores of death were calculated with multivariate logistic regression analysis (28 (link)) According to the T staging, N staging and ADAM17 values, the risk score of each patient (ranging from 0 to 100) was calculated. The receiver operating characteristic (ROC) curve was applied, and the area under the curve (AUC) was determined by the MedCalc statistical software version 11.0 (MedCalc, Mariakerke, Belgium). Data were presented as the mean ± standard deviation, and the differences between two groups were compared using the Student’s t-test and among three or more groups were analyzed by one-way analysis of variance, followed by Fisher’s least significant difference test. Statistical analyses were carried out using SPSS 17.0 (SPSS, Inc., Chicago, IL, USA). P<0.05 was considered to indicate a statistically significant difference.
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4

Comparison of Blood Pressure Metrics

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A number of patients in each group was decided as per statistician's advice with pilot study results. Both these groups were compared and statistically analyzed using Student's t-test and significance evaluated by P value. Repeated – measures analysis of variance were used to compare measurements of BP and pulse over time. P < 0.05 considered as statistically significant. Statistical analysis carried out using MedCalc Statistical software version 11.0 (MedCalc software, Buba, Belgium).
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5

Statistical Analysis of Diagnostic Efficacy

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All continuous variables and patient characteristics were expressed as medians (interquartile range (IQR)) or n (%), as appropriate. Chi‐square or Fisher's exact tests were used to compare categorical data. The Mann‐Whitney U test and Kruskal–Wallis test was used to compare two or more groups. Correlations between CAP and continuous variables were assessed using Spearman correlation coefficients (ρ). Parameters that were significantly associated with CAP were subsequently entered into a multiple linear regression models (Stepwise method).
Receiver operating characteristic (ROC) curves were plotted, and areas under the curves (AUROCs) with 95% confidence interval (CI) were calculated to determine the diagnostic efficacy. The accuracy of CAP at optimal thresholds was defined by the maximum Youden index. For each optimal cut‐off value, the sensitivity, specificity, positive predictive values (PPVs) and negative predictive values (NPVs), positive likelihood ratio (PLR) and negative likelihood ratio (NLR) were calculated. The general coincidence rate was calculated as (true positive + true negative)/overall patients.
All statistical analyses were performed using spss version 16.0 for windows (SPSS, Chicago, IL, USA) and medcalc statistical software version 11.0 (MedCalc, Mariakerke, Belgium). Two‐sided P‐values <0.05 were considered statistically significant.
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6

Stool Sample Analysis: PETIA, ELISA, and Semi-Quantitative Assay

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A total number of 95 stool samples for PETIA and ELISA and 53 stool samples for PETIA and the semi-quantitative assay were included for the agreement assessment between the different methods. Concentrations above the highest calibrator were diluted 1:10 with distilled water. Using the mean concentrations calculated from the duplicate measurements of each sample, Passing–Bablok regression analysis and Bland-Altman plots were performed from the PETIA with the mean concentration measured by the ELISA and the semi-quantitative lateral flow assay using MedCalc statistical software version 11.6 (MedCalc, Ostend, Belgium).
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7

Genetic Associations in Inflammatory Bowel Disease

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Statistical analysis was performed using MedCalc statistical software version 11.6. The Hardy-Weinberg equilibrium test was performed separately for patients and controls to measure the distribution of polymorphisms. The association between IBD (CD and UC) and IL23R (L301P) ATG16L1 (T300A) genotypes was determined by Fisher's exact test (Odds Ratio with Confidence interval (CI) at 95%). The χ2 test or Fisher test was used to correlate the IL23R and ATG16L1 polymorphisms and clinical parameters. The P value (<0.05) was considered statistically significant in all variables.
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8

Serum Iron as HCC Survival Predictor

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The MedCalc statistical software version 11.3.0.0, combined with the subject operating characteristic (ROC) curve, were used to determine the optimal cut-off value for the assessment of HCC survival outcomes with serum iron. SPSS 24.0 software was used for statistical data analysis. Continuous variables conforming to the normal distribution were expressed as mean ± standard deviation (SD) and assessed with Student’s t-test. The classified data were compared by Pearson chi-square test or Fish exact test. Correlation analysis was used to evaluate the relationship between preoperative serum iron levels and other clinicopathological indicators in HCC patients. Kaplan–Meier method and log rank test were used to analyze the OS, DFS and recurrence rate of patients. Univariate analysis and multivariate COX hazards regression model were used to determine the prognostic factors related to OS and DFS. P < 0.05 was considered statistically significant.
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9

Survival Analysis of Cancer Patients

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SPSS13.0 (SPSS Inc, Chicago, IL) and MedCalc statistical software version 11.3.0.0 (MedCalc Software, Broekstraat 52 Mariakerke, Belgium) were used in analyzing the data. The Pearson χ2 test was used to compare qualitative variables. Univariate analysis was performed to determine the significance of variables using the logistic regression model for the response rate and the Cox regression model for DFS and OS. Survival curve was estimated by Kaplan-Meier analysis, and the log-rank test was used to examine the difference of survival distributions between groups. Subsequently, the variables with P < .05 were subjected to multivariate analysis. Cox proportional hazards regression model was used to determine the independent prognostic factors. A value of P < .05 was considered significant.
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10

Multivariate Survival Analysis Protocol

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SPSS13.0 (SPSS Inc., Chicago, IL) and MedCalc statistical software version 11.3.0.0 (MedCalc Software, Broekstraat 52 Mariakerke, Belgium) were used for data analysis. The Pearson χ2 test was used to compare qualitative variables. Univariate analysis was first performed to determine the significance of variables using the logistic regression model for the response rate and the Cox regression model for DFS and OS. Survival curves were estimated by Kaplan–Meier analysis, and the log-rank test was used to examine differences in survival distributions between groups. Subsequently, the candidate variables from the univariate analysis with P < 0.05 were subjected to multivariate analysis. Cox proportional hazards regression model was used to determine the independent prognostic factors. A value of P < 0.05 was considered statistically significant.
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