Baseline characteristics of irMG group were evaluated using frequencies and percentages for categorical data, while median and range were used to describe continuous data. Comparisons of categorical variables between control group and patient group were tested for significance using the x2 test. Continuous variables were compared using the Mann-Whitney U test. We performed single variate binary logistic regression analyses to determine the odds ratios (ORs) for associations between certain clinical or demographic factors and risk of unfavorable outcomes for irMG. Factors that were significantly associated with an unfavorable outcome were analyzed together in a multivariate binary logistic regression model. This analysis was performed with the maximal level of adjustment. All tests were 2-sided, and Bonferroni correction was applied to the α level to adjust for multiple comparisons. Bonferroni-adjusted p values are reported in the tables. Statistical analyses were carried out using the SPSS 24.0 statistical package (SPSS; Chicago, IL, USA). The study was approved by the local ethics committee.
Spss 24.0 statistical package
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SPSS 24.0 is a statistical software package developed by IBM. It provides a variety of data analysis and statistical modeling tools, including descriptive statistics, regression analysis, and multivariate techniques. The software is designed to help users analyze and interpret complex data sets.
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24 protocols using spss 24.0 statistical package
1
Analyzing Outcomes in Immune-Related Myocarditis
2
Statistical Analysis Protocol for Comparative Study
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To assess differences between groups, the Chi-square test for qualitative variables and ANOVA followed by DMS post-hoc tests for quantitative variables were used. To evaluate the correlations, Spearman’s correlation test and regression analysis were performed. Significance was accepted at the level of p < 0.05. The Bonferroni correction was used for multiple comparisons. Logistic regression was used to predict the ROC curves and the Chi-squared test for ROC area comparison. Statistical analyses were performed with SPSS 24.0 statistical package.
3
Associations of Brain EPVS with Motor Performance
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Statistical analysis was performed using the SPSS 24.0 statistical package (SPSS Inc., Chicago, IL, USA) and Prism 9. Categorical variables as numbers and percentages, and continuous variables are presented as means and standard deviations, non-normal distributed variables as medians and interquartile ranges. Analyses of differences in motor performance and clinical information based on different burdens of EPVS in CSO and BG. One-way analysis of variance and Kruskal–Wallis test was used for continuous data, and chi-square test or Fisher exact test for categorical data.
The relationships between BG-EPVS and CSO-EPVS and motor performance were evaluated using binary logistic regression analysis with EPVS as a determinant and motor parameters (gait velocity, stride width, number of steps, step length, time of TUG, SPPB and Tinetti score, time of pronation–supination, time of finger-tapping and time of opening and closing hands) as outcome variables. Model 1 was adjusted for age, sex, height, and BMI, and Model 2 was the same as Model 1, with further adjustments for WMH judged by Fazekas score, presence of lacunar, brain atrophy, and CMB and other relevant risk factors. Statistical significance was established at P < 0.05.
The relationships between BG-EPVS and CSO-EPVS and motor performance were evaluated using binary logistic regression analysis with EPVS as a determinant and motor parameters (gait velocity, stride width, number of steps, step length, time of TUG, SPPB and Tinetti score, time of pronation–supination, time of finger-tapping and time of opening and closing hands) as outcome variables. Model 1 was adjusted for age, sex, height, and BMI, and Model 2 was the same as Model 1, with further adjustments for WMH judged by Fazekas score, presence of lacunar, brain atrophy, and CMB and other relevant risk factors. Statistical significance was established at P < 0.05.
4
Prognostic Factors for Survival Outcomes
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The probabilities of overall survival (OS) and progression-free survival (PFS) were estimated using the Kaplan–Meier method. The Cox proportional hazards model was used to identify prognostic factors. Prognostic factors with P values < 0.1 in univariable analysis were further assessed in multivariable analysis. The hazard ratios (HR) and 95% confidence intervals (CI) were calculated using the Wald test. P values < 0.05 were considered statistically significant. The SPSS 24.0 statistical package (SPSS Inc., Chicago, IL, USA) was used for statistical analysis.
5
Physiological Assays in Biological Triplicates
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All the phenotype observations and physiological assays were performed in biological triplicates. Data were presented as mean ± standard error (SEM). Statistical analysis was performed with an SPSS 24.0 statistical package (SPSS Inc., Chicago, IL, USA). One-way ANOVA was carried out with multiple comparisons using Duncan’s test to evaluate significant differences at 0.05 probability level.
6
Statistical Analysis of Clinical Outcomes
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SPSS 24.0 statistical package (SPSS, Chicago, IL, USA) was used for data processing and statistical analysis. Student t-test was used to evaluate continuous variables and Chi-square test was used to analyze categorical variables. Univariate logistic regression analysis was carried out to detect potential factors related to clinical outcomes. P<0.05 was considered statistically significant.
7
Predictive Factors of Neonatal Morbidity
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A univariate analysis of the potentially predictive factors was carried out by using the Chi-squared tests to calculate the categorical variables, stratified for nulliparous and multiparous women (Table 1 and Table 2 ). Then, a multivariate analysis was performed by binary logistic regression, where all the variables considered potential risk factors of neonatal morbidity for both nulliparous and multiparous women were used. The statistical analysis was performed in a stratified way for nulliparous and multiparous, since the parity variable is considered an effect modifying factor.
The “primary outcome” variable was a composite of neonatal morbidity (CNM yes/no) (Table 3 ). The results were analysed using the SPSS 24.0 statistical package (SPSS Inc., Chicago, IL, USA).
The “primary outcome” variable was a composite of neonatal morbidity (CNM yes/no) (
8
Prognostic Value of Lymphocyte-to-White Blood Cell Ratio
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Statistical analysis was performed using the SPSS 24.0 statistical package (SPSS Inc., Chicago, IL), R software version 4.1.0 (http://www.r-project.org/ ), and X-tile software (Version 3.6.1, Yale University, New Haven, CT, United States). Continuous variables were compared using the t test or the Mann-Whitney U test, whereas categorical variables were compared using the chi-square test or Fisher’s exact test. Univariate analysis and multivariate Cox proportional hazards models were performed to identify whether LWR was related to poor outcomes. The optimal cutoff value of LWR was determined by using X-tile. The Kaplan-Meier survival curve was generated by the “survival” and “survminer” packages in R software. All statistical tests were two-sided with a statistical significance level set at P values < 0.05.
9
Predictive Model for Portal Vein Thrombosis
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Categorical variables are expressed as frequency and percentage, and the significance was determined using χ2 or Fisher’s exact test. Quantitative variables are expressed as mean ± standard deviation, and the significance was determined using the Student’s t-test. Non-normally distributed variables are expressed as a median and interquartile range, and the significance was determined using the Mann-Whitney U test. The quantitative variables were converted to categorical variables based on the cut-off values. Multivariate logistic regression analyses were performed to identify the independent risk of PVT. The discrimination of the nomogram was measured by calculating the area under the receiver operating characteristic (AUROC) and the concordance index. The model calibration was determined using the Hosmer-Lemeshow technique and calibration curve. Internal validation was performed using bootstrap resampling. We fit the model repeatedly in 1000 bootstrap samples and evaluated its performance on the original samples. Differences were considered significant at P <0 .05. Analyses were performed with the SPSS 24.0 statistical package and R version 3.6.1. The risk of bias and reporting quality for this study were assessed against Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis and the Prediction model Risk of Bias Assessment Tool.
10
Comparative Analysis of Treatment Outcomes
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Quantitative data are shown as mean (standard deviation) and compared using independent Student’s t-test; qualitative variables are shown as frequency and percentage and compared using the Chi-square test. An a priori power analysis was performed as a component of design to estimate the required total sample size as a function of power 1- β = 0.80, with a medium effect size of 0.55 and α = 0.05. Power calculation was computed using G power version 2 (Franz Faul and Edgar Erdfelder). The data were analyzed using the SPSS 24.0 statistical package. All statistical analyses were two-tailed and a p-value <0.05 was considered statistically significant.
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