R statistical computing environment

Unless indicated otherwise, the continuous variables were described as median (range) and groups were compared with the Mann–Whitney U‐test, Kruskal–Wallis test or Wilcoxon ranked sum test as appropriate. Regression analyses were performed on R Statistical Computing environment (R Foundation for Statistical Computing, Vienna, Austria. URL http://www.R‐project.org) using EZR on R commander.⁴⁶ All other statistical tests were performed on GraphPad Prism for macOS version 8.4.3.

Normality of the data was tested using the Shapiro–Wilk test. Depending on the distribution, data are presented either as the mean ± SD or as the median and min–max range. Categorical variables are reported as frequencies and percentages (%). Mean values were compared using t-tests, whereas non-parametric data were compared using Mann–Whitney’s U-test (between the CAD and NCAD groups). Differences between DNA methylation and mRNA levels in EAT and SAT were assessed using Wilcoxon’s signed-rank test. Pairwise comparisons were Holm-Bonferroni corrected for four comparisons (two paired, two unpaired). Categorical variables were analyzed by Fisher's exact test. Spearman correlation testing was performed between methylation levels, mRNA and clinical and biochemical markers. Statistical analysis was performed using SPSS 17.0 software (SPSS Inc., Chicago, IL, USA) and in R statistical computing environment (the R Foundation, Vienna, Austria). Results with a p value < 0.05 were considered to be statistically significant.

We analyzed quantitative data using the R statistical computing environment (R Foundation for Statistical Computing) [30 ]. Sociodemographic characteristics of the sample, the intention-to-treat abstinence rates, and other cessation outcomes were compared by study arm using Pearson chi-square and t tests. We used descriptive statistics to summarize program acceptability and engagement results. All tests of statistical significance were 2-tailed, and P<.05 was considered significant. The process for qualitative analyses was the same across the 3 aims and described above.

The data were analyzed using SPSS Statistics version 21.0 (SPSS Inc., Chicago, IL) and R Statistical Computing Environment (R Foundation for Statistical Computing, Vienna, Austria). Continuous variables are expressed as the mean and standard deviation, and categorical data are expressed as counts and percentages for survival analysis. The baseline characteristics and group differences were compared using Pearson’s chi-square test for proportions. Survival analysis was performed using the Kaplan-Meier method (with the log-rank test) and the Cox proportional hazards model. Survival was estimated in months from the date of the diagnosis of gastric GIST to the date of death (for nonsurvivors) or the last follow-up (for survivors). T test was used for continuous variables. P value < 0.05 was considered statistically significant. All P values were two-tailed, and all confidence intervals (CIs) were 95% CIs.

Wilcoxon tests were used to compare mean differences between tumor and biopsy samples for phyla, genera and OTU log-abundances. Considering t = total number of taxa tested, p = raw p-value and R = sorted rank of the taxon, P-values were corrected for multiple testing (Sanapareddy et al., 2012 (link)) using:
Fold changes for each genera/OTU were calculated using:
Chi-Square tests were performed on subject's categorical data such as gender, alcohol and tobacco use and vital status. Student t-tests were performed to compare differences in the means between both groups for age, height, weight, BMI, and alpha diversity. We used ANOSIM and ADONIS (Oksanen et al., 2016 ) to compare differences in beta-diversity between groups using 3 distance metrics weighted UniFrac, unweighted UniFrac and Bray-Curtis for categorical, and numerical variables, respectively. Linear models were built using normalized counts at the genera and OTU level to investigate associations with clinical-pathological characteristics of rectal-cancer samples, such as lymph node and perineural neoplastic invasion status. Unless otherwise stated, values were reported as mean ± SD (standard deviation) and P-values <0.05 were considered statistically significant. All calculations were performed within the R statistical computing environment (R Foundation, 2011 ) unless otherwise stated.