All data were analyzed using SPSS statistics software Version 15 (SPSS Inc., Chicago, Illinois, United States). Chi-square test and binary logistic model analysis were also used. Statistical significance was at
p <0.01.
Spss statistics software version 15
Manufactured by IBM
Sourced in United States
SPSS Statistics is a software package used for statistical analysis. Version 15.0 provides a range of statistical analysis tools and techniques for data management, analysis, and presentation. The software is designed to handle a variety of data types and offers features for data exploration, modeling, and reporting.
Automatically generated - may contain errors
Lab products found in correlation
6 protocols using spss statistics software version 15
1
Statistical Analysis of Experimental Data
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All data were analyzed using SPSS statistics software Version 15 (SPSS Inc., Chicago, Illinois, United States). Chi-square test and binary logistic model analysis were also used. Statistical significance was at
p <0.01.
+ Expand
All data were analyzed using SPSS statistics software Version 15 (SPSS Inc., Chicago, Illinois, United States). Chi-square test and binary logistic model analysis were also used. Statistical significance was at
p <0.01.
2
Hyperglycemia and Stroke Outcomes
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Patient characteristics were compared between patients with and without hyperglycemia on admission. Categorical variables were studied with chi-square test. Continuous variables were analyzed using unpaired t-test when they are normally distributed. If not normally distributed a Mann-Whitney U test was used. Normality of data was visually inspected. Variables were indicated statistically significant when p < 0.05.
Hyperglycemia and glucose levels on admission were related to primary outcome measures with univariable linear regression. Based on comparable literature, adjustments were made for age, sex, NIHSS on admission, onset-imaging time and diabetes mellitus with multivariable linear regression analyses. In addition, admission glucoses levels were subdivided in quartiles and were related to the primary outcome measures. The association between patients with hyperglycemia and outcome at 3 months was expressed as odds ratios (OR) with corresponding confidence intervals. Adjustments were made for age, sex, baseline NIHSS, onset-imaging time and EVT using multivariate logistic regression.
Statistical analyses were performed using SPSS statistics software version 15.
Hyperglycemia and glucose levels on admission were related to primary outcome measures with univariable linear regression. Based on comparable literature, adjustments were made for age, sex, NIHSS on admission, onset-imaging time and diabetes mellitus with multivariable linear regression analyses. In addition, admission glucoses levels were subdivided in quartiles and were related to the primary outcome measures. The association between patients with hyperglycemia and outcome at 3 months was expressed as odds ratios (OR) with corresponding confidence intervals. Adjustments were made for age, sex, baseline NIHSS, onset-imaging time and EVT using multivariate logistic regression.
Statistical analyses were performed using SPSS statistics software version 15.
3
Predictors of Pancreatic Autoantibodies Post-Transplant
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Categorical variables were described using percentage and quantitative variables depending on if they followed a normal distribution (mean ± standard deviation [SD]) or not (median, interquartile range). Non-parametric data were analyzed by the Mann–Whitney U-test or Kruskal-Wallis test. The correlation between CD14+CD16+ monocytes and other variables was calculated by the Pearson correlation coefficient method. Multivariate logistic regression was used to evaluate the association between different factors and post-transplant pancreatic autoantibodies positivity; we first performed a univariate analysis, and then the multivariate model was constructed including significant variables and those that may be related to autoantibody positivity. Goodness-of-fit (calibration and discrimination ability) was performed using the Hosmer-Lemeshow test and the ROC curve. The area under the curve (AUC), sensitivity, specificity, positive and negative predictive values and the accuracy of the model were also assessed. For all tests, statistical significance was assumed at P < 0.05. Data were analyzed using SPSS Statistics software version 15.0 (SPSS, Inc., Chicago, Ill, United States) or the GraphPad Prism 6.0c (GraphPad Software, La Jolla, CA).
4
Inflammatory Stage Correlation Analysis
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Statistical analysis was performed using SPSS statistics software version 15.0 (SPSS, Inc., Chicago, IL, USA). The patient demographic and clinical characteristics are presented as the mean ± standard deviation (SD) for continuous data and n (%) for categorical data. Morphological data are presented as n (%) by inflammatory stage. The differences among the inflammatory stages were compared using Fisher’s exact test. Spearman correlation analysis was performed to identify correlations between the morphologic data and inflammatory stage. Results are presented as the coefficient of correlation (r) and the corresponding P-value. Statistical assessments were two-tailed and significance was set at P<0.05.
5
Statistical Analysis Methods for Research Protocols
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Results were presented as the mean ± SD (for normally distributed data) or median and interquartile range (IQR, for non-normally distributed data), as appropriate. Difference in the paired samples was assessed using paired t test or Wilcoxon’s signed rank tests, as appropriate. Difference between unpaired groups was assessed using t tests or nonparametric tests, as appropriate. Difference between multiple groups was assessed using the one-way ANOVA. Difference between non-independent data was assessed using generalized estimating equations. Statistical analyses were performed with the SPSS statistics software, version 15.0 (Chicago, IL, USA). The p values less than 0.05 were considered significant.
6
Statistical Analysis of Research Data
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A descriptive statistic of the variables collected was performed. Data of continuous variables were expressed as the mean ± standard deviation or median and interquartile range according to whether they followed or not a normal distribution. Categorical variables are shown as absolute values and percentage. Parametric and nonparametric comparisons of group differences, such as the T-student test or the Mann-Whitney tests were used for quantitative variables where appropriate. Chi-square analysis was used to evaluate differences between proportions. Data were analyzed using SPSS statistics software version 15.0 (SPSS, Inc., Chicago, IL). Two-sided P values of <0.05 were considered statistically significant.
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