Spss statistics 24

Statistical analyses were conducted with SPSS Statistics 24 (SPSS Inc., an IBM company). Quantitative data were expressed as median values. Independent samples t test was used for comparison between two groups. Correlation between parameters was analyzed by Spearman’s rank correlation coefficient. All statistical analyses were performed with and SPSS Statistics 24 (SPSS Inc.). P < 0.05 was considered statistically significant.

The Log-rank (Mantel–Cox) test was used for survival analysis by GraphPad Prism 7.0. The link between SNORNAs’ expression and clinicopathologic features of ovarian cancer patients was assessed using the Chi square test and unpaired t test by SPSS Statistics 24.0 software. HRs were calculated using the Cox proportional hazard model, and 95% confidence intervals (CI) were also determined by SPSS Statistics 24.0 software. All other analyses were performed with GraphPad prism7.0 using unpaired t test. Differences were considered statistically significant when the P-value was < 0.05.

Statistical analysis was performed using IBM SPSS Statistics 24 (SPSS Inc., Chicago, IL., USA). Values were tested for Gaussian distribution using the Kolmogorov–Smirnov test. Bilateral comparisons between groups were performed using Student’s t-test for normally distributed data or Mann–Whitney U-test for non-normally distributed data and Chi-square test for categorical data, as appropriate. Multiple group comparisons were performed using ANOVA or Kruskal–Wallis test. Correlations were calculated with bivariate Pearson correlation or Spearman correlation, as appropriate. For multiple correlation analyses, Bonferroni correction was applied. Binary logistic regression was performed using the stepwise backwards method. ROC analysis was conducted to determine the biomarkers’ ability to discriminate AF from ESUS. A p-value of <0.05 was regarded as significant. Figures were created using GraphPad Prism 5 (GraphPad Inc., La Jolla, CA, USA) and IBM SPSS Statistics 24 (SPSS Inc., Chicago, IL., USA). Figures show Boxplots with Tukey Whiskers, unless otherwise stated.

The RLU and CFU levels are expressed as the mean ± standard deviation. All experiments were repeated independently three times. The statistical significance of differences among the A, B and C groups was determined using a one-way analysis of variance (ANOVA) and Kruskal–Wallis test (IBM SPSS statistics 24, IBM Corporation, Armonk, NY, USA). Differences between two groups were determined using a post hoc test (Bonferroni, IBM SPSS statistics 24). A p-value less than 0.05 was considered significant.

Continuous variables were presented with mean and standard deviation, and categorical variables were presented with percentage. Differences between groups were tested on the basis of independent-samples t-test or chi-square test using SPSS Statistics 24.0 (IBM Canada Ltd.). Departure from Hardy–Weinberg equilibrium (HWE) in NCs was calculated on the basis of chi-square test using SPSS Statistics 24.0 (IBM Canada Ltd.). Associations between SNPs and PTC/NG under five models of inheritance (codominant, dominant, recessive, overdominant, and allele model) were performed using online SNPStats program (http://bioinfo.iconcologia.net/SNPStats) [24 (link)]. Strength of association was assessed by odds ratio (OR) and corresponding 95% confidence intervals (CIs). The best model of inheritance for each SNP was selected on the basis of the Akaike information criterion (AIC). Bonferroni correction was performed to reduce type I error in multiple testing in view of 15 comparisons between groups (three SNPs × five models); thus, significant P value was set at 0.05/15 = 0.003 for genotype analyses. Linkage disequilibrium analysis between the three SNPs and haplotype analysis was analyzed using the online SNPStats program. Power calculations were done using Quanto 1.2.4 software. Two-sided test with P value less than 0.05 was considered statistically significant otherwise specified.