Demographic, biophysical and sleep data and neuropsychological scores were compared between the two groups using the two-samples t-test with Statistical Package for the Social Sciences version 24.0 (SPSS, Chicago, IL, USA). Then, we used two independent-sample t-tests to analyze differences in the rs-FC maps among the groups by using the body mass index (BMI), age and education as covariables. Multilevel comparisons were corrected using Gaussian random field theory (GRF, two-tailed, voxel-level P<0.01 and cluster-level p<0.05). Finally, the relationships between the average z-values of each brain region with significant intergroup differences and clinical indices in OSA patients were evaluated using Pearson correlation analysis. P<0.05 was considered statistically significant.
Statistical package for the social sciences version 24
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SPSS Statistics is a software package used for statistical analysis. Version 24.0 includes capabilities for data manipulation, statistical modeling, and reporting of results.
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Lab products found in correlation
Prism 5, by GraphPad (2 mentions)
Spss statistics for window, by IBM (2 mentions)
Version 12, by MedCalc (1 mentions)
Stata, by StataCorp (1 mentions)
Sigmaplot, by Grafiti LLC (1 mentions)
Statistical analysis software version 9, by SAS Institute (1 mentions)
Prism 7, by GraphPad (1 mentions)
R statistical software, by R Project for Statistical Computing (1 mentions)
Lightcycler software, by Roche (1 mentions)
Statistical software version 15, by MedCalc (1 mentions)
174 protocols using statistical package for the social sciences version 24
1
Neuropsychological Assessment in OSA
2
Assessing Healthcare Professionals' Sarcopenia Knowledge
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Data were analysed using Statistical Package for the Social Sciences, version 24.0 (SPSS Inc). Continuous variables were checked for normality and presented as mean (standard deviation [SD]) if normally distributed and as median (interquartile range [IQR]) if skewed. Categorical variables were presented as number (n) and percentage (%). t tests and chi‐square tests were used to compare the characteristics of health‐care professionals who did and did not complete the follow‐up questionnaires and between health‐care professionals dependent on their knowledge of sarcopenia. Visualisation of results was performed using GraphPad Prism 5.01.
3
Statistical Analysis Methods for Predicting Risk Factors
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Statistical analysis was performed using the Statistical Package for the Social Sciences version24.0 (SPSS Inc, Chicago). Categorical variables were compared using chi-square test or Fisher’s exact test expressed as frequency and percentage. Continuous variables were tested for normal distribution by the Kolmogorov Smirnov test, then compared between groups with a t-test among the normal distribution variables and expressed as the mean ± standard deviation. Those with skewed distributions were compared with the Mann-Whitney test and expressed as medians and interquartile range. Three groups of continuous variables in accordance with the normal distribution were compared by ANOVA test, and variables with skewed distributions were compared by the Kruskal-Wallis H test.
Univariate and multivariate logistic analyses were performed to predict the risk factors for PA. Survival was estimated by the Kaplan-Meier method and compared using log-rank tests. Multivariate Cox proportional hazards regression analysis was used to calculate the hazard ratio (HR) and 95% CI. In all analyses, a two-tailed P<0.05 was considered statistically significant.
Univariate and multivariate logistic analyses were performed to predict the risk factors for PA. Survival was estimated by the Kaplan-Meier method and compared using log-rank tests. Multivariate Cox proportional hazards regression analysis was used to calculate the hazard ratio (HR) and 95% CI. In all analyses, a two-tailed P<0.05 was considered statistically significant.
4
Clinicopathological Factors and Survival Analysis
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All statistical analyses were performed using the Statistical Package for the Social Sciences, version 24.0 (SPSS Inc., Chicago, IL, USA). The correlation between clinicopathological factors and treatment groups were evaluated using the chi-square test for categorical variables and Student’s t test for continuous variables. Univariate and multivariable logistic regression models were used to evaluate the independent predictors of CRM involvement. Disease free survival (DFS) and overall survival (OS) were examined using the Kaplan–Meier method, and the log-rank test was used to compare time-to-event distributions. OS was defined as the duration between date of primary treatment and date of death from any cause or to the last follow-up date. DFS was defined as the duration between date of primary treatment date to the date of recurrence or metastasis or to the last follow-up date. A Cox proportional hazard model was used for multivariable analyses to identify the independent prognostic factors for OS and DFS. All tests were two-tailed, and a P value of less than 0.05 was considered statistically significant.
5
Survival and Gene Expression in Labeo rohita
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Survival data of Labeo rohita fingerlings were arcsine-transformed to satisfy normality and homoscedasticity requirements, as necessary. The data were then subjected to one-way analysis of variance followed by Duncan’s multiple range test using the Statistical Software Statistical Package for the Social Sciences version 24.0. P-values smaller at p < 0.05. Gene expression results are presented as fold change relative to the internal control gene (β-actin). Statistical analysis for significant differences in the expression levels was performed with single-tailed Student’s t-tests using log-transformed data.
6
Geriatric Syndromes and Health Factors
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All data were categorical variables. To describe the demographic information, descriptive statistics were presented as counts and percentages. The Statistical Package for the Social Sciences (version 24.0) was used to enter and analyse the counts and percentages. The chi-squared test was utilized to evaluate the association between demographic information and self-related health with the presence of multiple geriatric syndromes. Statistical significance was set at P < 0.05 (two-tailed). To enhence clinical interpretability, adjacent groups with similar clinical significance and a sample size of less than 5% within the group were merged.
A Logistic regression model was conducted using the multiple geriatric syndromes as dependent variables and other factors as independent variables. The B, SE (standard error), Wald, 95% CI, and P values were reported. The results of the model were presented as odds ratios (ORs) with 95% confidence intervals (CIs).
A Logistic regression model was conducted using the multiple geriatric syndromes as dependent variables and other factors as independent variables. The B, SE (standard error), Wald, 95% CI, and P values were reported. The results of the model were presented as odds ratios (ORs) with 95% confidence intervals (CIs).
7
Validation of a Research Instrument
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Statistical Package for the Social Sciences, version 24.0 (SPSS, Inc., Chicago, IL), was used for statistical analysis. We used the qualitative approach for content validity. Spearman correlation coefficient was used for convergent validity (because the distribution of the data was not normal, so the nonparametric test was used); Mann-Whitney test (because the distribution of the data was not normal) was run for known-groups validity; and Intra-class correlation coefficient was implemented to calculate inter-rater agreement and test-retest reliability. Kuder-Richardson test 21 was used for the internal consistency.
8
Hcy and PCOS Correlation Analysis
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Based on the linear regression between the variables, we used Pearson's correlation coefficient r to define the dependence of Hcy on the HOMA-IR in the patients studied. We used the One-Way ANOVA test to compare hormonal, clinical, and ultrasound parameters between the groups of PCOS. The methodology used in our study follows the most applied guidance for statistical analysis in the medical sciences (21). To simplify statistics and increase the perceptibility of the tables providing the statistical analysis results, we expressed the level of variability of the analyzed variables by showing the value of their standard deviation. Statistical analysis was done using office software MS Excel 2021 and Statistical Package for the Social Sciences, version 24.0 (SPSS 24.0, Chicago, IL, USA). A p-value of 0.05 or lower is considered statistically significant.
9
Statistical Analysis of Continuous Data
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The data were presented as mean ± standard deviation with range for the continuous data with normal distribution and median and interquartile range for those not. One-sample Kolmogorov–Smirnov test was applied to assess whether the data are normally distributed or not. Statistical analysis was completed by Statistical Package for the Social Sciences, version 24.0 (SPSS Inc., Chicago, IL, USA).
10
Acute Epiglottitis Diagnosis Validation
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Continuous variables are presented as mean and standard deviation and were compared using the Student t test. Categorical variables are presented as proportions and were compared using the Chi-squared test. The sensitivity, specificity, and receiver operating characteristic (ROC) curve were analyzed for the measured parameters and the ratio of each parameter to the C3W. We performed 5-fold cross-validation for internal validation of radiologic parameters demonstrating good or excellent performance with ROC curve analysis. After all patients were randomly partitioned into five sets, the cutoff value of each radiologic parameter for diagnosing acute epiglottitis was cross-validated. A P-value of less than .05 was considered statistically significant. Data were analyzed mainly using the Statistical Package for the Social Sciences version 24.0 (SPSS Inc, Chicago, IL, USA). MedCalc version 12.2 (MedCalc Inc, Mariakerke, Belgium) was used to produce an interactive dot diagram of the ROC curve. The comparison of areas under the ROC curve (AUROCs) was performed by the method of Hanley & McNeil where the p values are adjusted by Bonferroni correction.[15 (link)] STATA (STATA Corporation, College Station, TX, USA) was used for cross-validation.
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