Data are presented as median values (± range) and absolute count (percentage), as appropriate. Differences in categorical variables between patients with or without cancer were evaluated with univariate analysis using Fisher’s exact test. A two-sided P value <.05 was considered statistically significant. Data were analyzed using IBM SPSS statistical software 24.
Spss 24 statistical software
Manufactured by IBM
Sourced in United States
SPSS 24 is a statistical software package developed by IBM. It is designed to analyze and manage data, perform advanced statistical analyses, and generate reports. The software provides a comprehensive set of tools for data manipulation, visualization, and modeling. SPSS 24 is widely used in various industries and academic institutions for research, decision-making, and data-driven insights.
Automatically generated - may contain errors
35 protocols using spss 24 statistical software
1
Categorical Variable Analysis in Oncology
2
Anthropometric Measurements and BMI Analysis
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Combined data were analysed by IBM SPSS statistical software-24 (SPSS Inc., Chicago, Illinois, USA). Descriptive statistics are presented as mean (SD) and percentage. We compared categorical variables using χ2test and independent t-test for continuous variables. We compared age-adjusted means for anthropometric measurements using analysis of covariance (ANCOVA). The relationship between BMI and associated variables (location, gender, age, education, marital status, and occupation) were tested using linear regression model. In addition, age-adjusted prevalence ratio was calculated in STATA version 14 by generalized linear model (log-binomial regression) logarithmic link function. A p value of <0.05 was considered statistically significant.
3
Evaluating Nociception and Molecular Changes
4
Nucleoli count analysis for BCSS
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The optimal cut-off point of nucleoli count against BCSS was defined using X-tile bioinformatics software version 3.6.1 (School of Medicine, Yale University, New Haven, USA) 33 . Nucleoli were given three scores depending on these cut-off points (Supplementary Table 2). Also, new grade scores of Nottingham grading system were obtained using cut-off points of total scores against BCSS using X-tile. IBM-SPSS statistical software 24.0 (SPSS, Chicago, IL, USA) was used in statistical analysis. The degree of inter-observer agreement was assessed through intra-class correlation coefficient (ICC) for continuous data. Fleiss' Kappa statistic was used to assess the concordance between more than two observers for categorical variables. Association between nucleoli count with different concordant and discordant cases was analyzed using Kruskal-Wallis test. Outcome analysis was assessed using Kaplan-Meier curves and the log-rank test. Cox proportional hazards multivariate regression modelling was used for the multivariate analysis. For all tests, p-values < 0.05 (two-tailed) were considered as statistically significant.
5
Bioinformatic Analysis of NOP10 Expression
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NOP10 expression for proteomic and transcriptomic was categorized using X-tile bioinformatics software version 3.6.1 (School of Medicine, Yale University, New Haven, USA) based on a prediction of BCSS [29 (link)]. IBM-SPSS statistical software 24.0 (SPSS, Chicago, IL, USA) was used for statistical analysis. Inter-observer agreement in NOP10 IHC scoring was assessed using intra-class correlation coefficient (ICC). The Chi-square test was performed for correlations between categorical variables. Spearman’s rank correlation coefficient was carried out to examine the association between NOP10 and other related markers. Univariate survival analysis was carried out using Kaplan–Meier curves and log rank test. Cox’s regression models were used for the multivariate survival analysis to adjust for confounding factors. For all tests, p-value < 0.05 was considered statistically significant.
This study followed the reporting recommendations for tumour markers prognostic studies (REMARK) criteria [30 (link)] (Supplementary Table S2).
This study followed the reporting recommendations for tumour markers prognostic studies (REMARK) criteria [30 (link)] (Supplementary Table S2).
6
Bariatric Surgery Outcomes Comparison
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Power analysis was performed to calculate the sample size necessary for our design based on a previous study10 (link). We achieved a power of greater than 90% for detecting the effects that we anticipate at a significance level of P < 0.05 with 6 subjects. All values are expressed as mean ± SEM. To account for small sample size, statistical comparison was performed between the lean group and pre, 1-month and 7-month individually using a Mann-Whitney U test. A Wilcoxon signed-rank test was used to compare differences between timepoints. Spearman correlation analysis was used to assess linear relationships. All calculations were performed with SPSS statistical software (24.0; SPSS, Inc., Chicago, IL). Significance was set at P < 0.05.
7
Obesity and EPS: Statistical Analysis
8
Predictive Biomarkers for Neurological Disorders
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SPSS statistical software 24.0 was used for the data analysis. The measurement data exhibiting a normal distribution are described as the mean ± standard deviation. Data that did not conform to a normal distribution were compared using the Kruskal-Wallis H test and are expressed as the median, 25th percentile and 75th percentile. The count data are expressed as a percentage (%). The differences between two groups were compared using a chi-squared test. A receiver operating characteristic (ROC) curve analysis was performed using MedCalc 19.1 to evaluate the potential use of the IgG SR, IgG index, QALB and QIgG to predict the GBS and CIDP groups. Indicators with predictive ability were extracted from ROC curve analysis. The cut-off point corresponding to the specificity of the predictive index (98–99%) was used as the standard. Patients with values beyond the cut-off value and those in the control group were analyzed by a binary logistic regression to determine the baseline features of the 1–2% of patients with levels above the cut-off who were considered to have the most severe disease presentation. A linear regression analysis was used to analyse the correlation between the IgG SR and QALB. P < 0.05 was considered indicative of a statistically significant difference.
9
Bariatric Surgery Outcomes Comparison
10
Comparative Analysis of Imaging Biomarkers
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All data are expressed as the mean ± standard deviation. The significance of differences in the baseline characteristics, enhancement patterns, ITC parameters, and pathological results were compared by the chi-squared test or independent-sample t-test. Statistical significance was defined as a p-value < 0.05. All data analysis was performed by using SPSS statistical software 24.0 (SPSS, Chicago, IL, USA).
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