All of the experiments were repeated at least three times. The Data were presented as means ± SD. Statistical analysis was performed using SPSS for Windows, version 11.5. Statistical significance was determined using one-way ANOVA with a post hoc Bonferroni’s test. Significance was set to p values < 0.05.
Spss for windows version 11
Manufactured by IBM
Sourced in United States
SPSS for Windows, version 11.5 is a statistical analysis software package. It provides tools for data management, analysis, and presentation.
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196 protocols using spss for windows version 11
1
Comparative Analysis of Cell Viability
2
Synovial RBC Counts for Angiography
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All measurements were expressed as mean (range), and independent t-tests were performed using SPSS for Windows version 11.5 (SPSS Inc., Chicago, IL, USA). Receiver operating characteristic curve analysis using the Youden index was conducted to determine an optimal cut-off of total synovial RBC count and RBC counts per HPF for predicting the need for interventional angiography. p < 0.05 was considered statistically significant.
3
Anticoagulation Therapy Outcomes Comparison
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Data analysis was performed using SPSS for Windows, version 11.5 (SPSS Inc., Chicago, IL, US). Data were shown as mean±SD or median (min–max), where applicable. The mean differences between the groups were compared using Student’s t-test; the Mann–Whitney U test was applied for comparisons of medians. Nominal data were analysed by Pearson’s chi-square test. Degrees of association between continuous variables were evaluated using Spearman’s rank correlation analyses.
Whether the differences in HRQoL, HADS-A and HADS-D scores and annual number of hospital admissions between the warfarin and NOAC groups were to be continued or not were evaluated by analysis of covariance (ANCOVA) after adjustment for age, gender, time interval for anticoagulation and medical compliance. Multiple logistic regression analysis was applied for determining the difference between the warfarin and NOAC groups for bleeding after adjustment for all possible confounding factors (i.e. age, gender, anticoagulation time and medical compliance). Odds ratios and 95% confidence intervals for each independent variable were also calculated. A two-sided p value of <0.05 was considered statistically significant.
Whether the differences in HRQoL, HADS-A and HADS-D scores and annual number of hospital admissions between the warfarin and NOAC groups were to be continued or not were evaluated by analysis of covariance (ANCOVA) after adjustment for age, gender, time interval for anticoagulation and medical compliance. Multiple logistic regression analysis was applied for determining the difference between the warfarin and NOAC groups for bleeding after adjustment for all possible confounding factors (i.e. age, gender, anticoagulation time and medical compliance). Odds ratios and 95% confidence intervals for each independent variable were also calculated. A two-sided p value of <0.05 was considered statistically significant.
4
Statistical Analysis of Research Data
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Data analysis was performed using SPSS for Windows, version 11.5 (SPSS Inc., Chicago, IL, USA). Whether the distributions of continuous and metric discrete variables were normal or not was determined by Kolmogorov-Smirnov test. Continuous and metric discrete variables were shown as mean ± standard deviation (95% confidence interval [CI]) or median (minimum–maximum), where applicable. While the mean differences between groups were compared by Student's t-test, otherwise, Mann–Whitney U-test was applied for comparisons of the median values. Nominal data were analyzed by Pearson's Chi-square test or Fisher's exact test, where appropriate. A P < 0.05 was considered as statistically significant.
5
Statistical Analysis of Anthropometric Measures
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Data analysis was performed using SPSS for Windows, version 11.5 (SPSS Inc., Chicago, Illinois, USA). Data are shown as means ± standard deviations (SDs), or as numbers of cases with percentages, as appropriate. Student’s t-test was used to compare differences in age and anthropometric measures between the groups, and the Mann-Whitney U test was employed to compare VAS scores. Nominal data were analyzed using Fisher’s exact test. We employed the McNemar or Wilcoxon signed rank test, as appropriate, to determine whether differences evident between any two follow-up visits were significant. A p value less than 0.05 was considered to reflect statistical significance. The Bonferroni correction was applied to minimize Type 1 error upon all multiple comparisons (corrected p value, 0.05 divided by number of comparisons).
6
Relative Expression of RFRP Gene
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Data from relative expression of the RFRP gene
were subjected to the test of normality and analyzed
by one-way ANOVA (SPSS for Windows,
version 11.5, SPSS Inc., USA). Mean separation
was performed by post hoc LSD test at P=0.05.
were subjected to the test of normality and analyzed
by one-way ANOVA (SPSS for Windows,
version 11.5, SPSS Inc., USA). Mean separation
was performed by post hoc LSD test at P=0.05.
7
Statistical Analysis of Multiple Sclerosis
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Statistical analysis was performed using SPSS for Windows, version 11.5 (SPSS, Chicago, IL, USA). Medians with 95% confidence intervals (CI) were calculated. The differences between two groups were compared with the Mann-Whitney U test. Spearman rank was used to calculate correlations among variables. We used logistic regression analysis to assess links between MS as a dependent variable and other potential factors. The results were considered statistically significant at a p value < 0.05.
8
Comparative Statistical Analysis of Variables
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The Chi-square and Mann-Whitney U test were used to compare the categorical and continuous variables, respectively. In this study, p < 0.05 was considered statistically significant. All statistical analyzes were performed using SPSS for Windows (version 11.5; SPSS, Chicago, IL, USA).
9
Analyzing Monitoring Indexes using SPSS
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SPSS for Windows, version 11.5 (SPSS Inc., Chicago, IL, USA) was used. The SEP and MEP monitoring indexes are expressed as the standard deviation (SD), and were analyzed using the paired t-test. P < 0.01 was considered to be statistically significant.
10
Evaluating Genetic and Oxidative Factors
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Continuous variables are expressed as the mean ± standard deviation. Logarithmic transformation was applied to the data showing non-normal distribution. Group comparisons and trend were performed using the Student’s t-test, Pearson’s linear regression and one-way ANOVA. p < 0.05 was considered statistically significant. As mtDNA copy number displayed a non-linear distribution pattern, we changed it to a delta CT set for comparison; whereas oxidative stress markers displayed in a linear distribution. The contrast factor was applied in a one-way analysis of variances to test for linear trends displayed by the number of APOE4 allele number subgroups. Statistical analysis was performed using the Statistical Package for Social Science program (SPSS for Windows, version 11.5; SPSS, Chicago, IL, USA).
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