Statistical analysis was performed with SPSS Statistics software version 13 (SPSS Inc., USA). Data were presented as the mean ± SD or the mean rank. The repeated measures method was used to analyze the proliferation plot. The Kaplan-Meier method and log-rank tests were used to compare ccRCC patient survival based on dichotomized MRCCAT1 expression. Cox proportional hazards regression analysis was used to analyze the independent factors on the survival prognosis of patients with ccRCC. Differences between groups were analyzed by Student’s t-test or nonparametric Mann-Whitney U test. A value of P < 0.05 was considered significant.
Spss statistics software version 13
Manufactured by IBM
Sourced in United States
SPSS Statistics software version 13.0 is a comprehensive data analysis tool. It provides core statistical and analytical capabilities to help users understand data, identify trends, and make informed decisions.
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9 protocols using spss statistics software version 13
1
Survival Analysis of ccRCC Patients
2
Statistical Analyses in Biological Research
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Statistical analyses were conducted using GraphPad Prism 8 (GraphPad Software Inc., San Diego, CA, USA) or SPSS statistics software version 13 (SPSS Inc., USA). All quantitative data are expressed as the mean values ± SD of at least three independent experiments or samples. Fisher’s exact test two-sided analyze was used to analyze statistically significant differences. The Pearson’s correlation was used to measure the strength of association between two variables. Cox proportional hazards regression analysis was used to analyze the independent factors on the survival prognosis of patients with ccRCC. Differences between groups were analyzed by Student’s t test or nonparametric Mann–Whitney U test. Continuous variables were expressed as the median (95% confidence interval) or mean ± standard deviation (SD) as appropriate. All in vitro experiments were carried out at least in duplicate, and representative results are shown in the figures. All statistical tests were two sided, and p < 0.05 was considered significant (*p < 0.05, **p < 0.01, ***p < 0.001).
3
Reproducibility Assessment of Elasticity Modulus
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All analyses were carried out using IBM SPSS Statistics software, version 13.0 (SPSS Inc., Chicago, IL, USA). The Statistical Package for the Social Sciences (SPSS) version 13.0 statistical analysis software was adopted, and continuous variables were expressed as mean ± SD. Discrete variables were analyzed using the The reproducibility of the ME mean was assessed in 20 randomly selected subjects (patients or controls) whose ME mean values were measured independently by two different physicians and twice by the same physician, respectively. The repeatability evaluation adopted a linear correlation analysis and Bland-Altman plots. Bland-Altman plots were used to assess the inter-observer and intra-observer variability, based on the elasticity modulus value for each ROI averaged across five frames in each acquisition.
4
Muscle Stiffness and Passive Force Relationship
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All data analyses were performed using the IBM Statistical Package for the Social Sciences (SPSS) Statistics software, Version 13.0 (IBM, Armonk, NY, USA). Continuous variables were expressed as means § SD. The 95% confidence interval (95% CI) and intra-class correlation coefficient (1, 1) (ICC 1,1 ) were used to measure and assess the reliability of the Young's modulus test results. Generally, ICC 1,1 values within the ranges 0À0.40, 0.41À0.6, 0.61À0.79 and 0.8À1.0 indicate poor, moderate, good and excellent reliability, respectively (Cohen 1968) .
The EÀF relationship between the Young's modulus and the passive muscle force of each tested muscle at each loading cycle was analysed by least-squares linear fitting using the equation
where E is the Young's modulus of the muscle under a passive force F. E 0 represents the Young's modulus of the slack muscle, and k is the change rate of the Young's modulus with respect to the normalised muscle force (passive muscle force divided by muscle weight). Based on measured Young's modulus and muscle force data, E 0 , k and the coefficient of determination (R 2 ) of each regression line were calculated.
The EÀF relationship between the Young's modulus and the passive muscle force of each tested muscle at each loading cycle was analysed by least-squares linear fitting using the equation
where E is the Young's modulus of the muscle under a passive force F. E 0 represents the Young's modulus of the slack muscle, and k is the change rate of the Young's modulus with respect to the normalised muscle force (passive muscle force divided by muscle weight). Based on measured Young's modulus and muscle force data, E 0 , k and the coefficient of determination (R 2 ) of each regression line were calculated.
5
Diagnostic Evaluation of Serum CgA
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To verify the diagnostic value of serum CgA, receiver operating characteristic (ROC) curves were plotted, and the area under the curve (AUC) was calculated. SPSS statistics software version 13.0 was used for statistical analysis. Mann–Whitney method was used to compare the CgA levels between each group of patients and healthy controls. Fisher exact test or Chi’s test were used to analyze our data. Two-tailed test was used in all of statistic analysis. P < 0.05 was considered statistically significant.
6
Survival Analysis of AGBL2 Expression
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All data were analyzed with SPSS Statistics software (Version 13.0, Chicago, Illinois, United States). Relationships between AGBL2 and other parameters were studied using the chi-square test, Fisher’s extract test, or independent t tests. Disease-specific survival was analyzed using the Kaplan-Meier method. The log-rank test was used to analyze survival differences. Multivariate analysis was performed using the Cox proportional hazards model selected in forward stepwise. A P value of less than 0.05 was considered statistically significant.
7
Statistical Analysis of Survival Data
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All data were analyzed with SPSS statistics software (Version 13.0; Chicago, IL, USA). Continual data were analyzed by Student's t-test, and categorical data were analyzed by the chi-square test or Fisher's exact test. Cumulative survival of individual groups of patients was analyzed using the Kaplan-Meier method and tested by the log-rank test. Multivariate analysis was performed using the Cox proportional hazards model. P values less than 0.05 were considered statistically significant.
8
Multivariate Statistical Analysis Protocol
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Statistical analysis was performed using SPSS Statistics software version 13.0 (SPSS Inc.) and Prism 6.0 software for Windows (GraphPad Software). All data are presented as means ± SD of at least three independent experiments. Multiple groups were compared by analysis of variance (ANOVA) and Tukey's post hoc test, and Pearson's correlation coefficient (r) was used to determine correlation between any two parameters. p Values <0.05 were considered statistically significant.
9
Risk Factors for Microvascular Invasion
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Continuous variables were shown means ± standard deviation, which were compared by Student's t-test. Chi-square test was used to compare categorical variables. Univariate logistic regression model was used to explore possible risk factors associated with the presence of MVI. And variables with P value < 0.1 were subsequently incorporated into multivariate backward logistic regression to identify independent risk factors of MVI presence. The odds ratio (OR) and 95% confidence intervals (CIs) were presented. Statistical analysis was performed using IBM SPSS Statistics software version 13.0 (SPSS, Chicago, IL) and R software for Windows (version R-3.4.3, the R Foundation for statistical computing). A P < 0.05 indicated statistical significance and all statistical comparisons were two-sided.
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