Categoric variables were analyzed using Pearson's chi-square or Fisher exact tests where appropriate, and continuous variables were analyzed using the Mann-Whitney U and Kruskal-Wallis tests. All analyses were performed using R (R Foundation for Statistical Computing, Vienna, Austria; http://r-project.org) and RStudio software Version 1.1.463 (RStudio, Inc, Boston, Mass). Univariable logistic regression was conducted to determine evaluate covariates to be included in a model for anastomotic leak. Surgical year was tested as a possible covariate to account for temporal changes over the study period. As the variable of interest, ketorolac administration was selected a priori for inclusion in the multivariable model. Then, backwards stepwise elimination was performed via sequential evaluation of the Akaike information criterion. The final multivariable logistic regression model was used to determine if ketorolac use was associated with anastomotic leak in patients undergoing esophagectomy for a diagnosis of esophageal adenocarcinoma.
R studio software version 1
Manufactured by Posit
Sourced in United States
RStudio is an integrated development environment (IDE) for the R programming language. Version 1.2.1335 provides a comprehensive set of tools for data analysis, visualization, and project management. It offers a user-friendly interface, code editing, debugging, and package management capabilities to support the R programming workflow.
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Lab products found in correlation
13 protocols using r studio software version 1
1
Ketorolac and Anastomotic Leak in Esophagectomy
2
Photosynthesis Light Saturation Modeling
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The Michaelis Menten model was used for the identification of the light saturation point of photosynthesis. A third-order sigmoidal regression model was applied between yield, profitability, and PD. Data analysis was performed using generalized linear mixed models with the comparison of DGC means with a p-value of 0.05. The PD treatments and evaluation time were used as fixed effects and the block as a random effect. The best model was selected using the Akaike criterion with Bayesian information and maximum likelihood (Jaramillo- Barrios et al., 2019) (link). The results only interpreted the factors and interaction that showed significant differences and the significant individual effects when the interaction did not show significant differences. The R-studio software version 1.1.463 was used.
3
Comparative Analysis of Plant Cultivars
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All analyses were performed in four repetitions. The Shapiro–Wilk test and the Kolmogorov–Smirnov test were performed to test the normality of all variables. All tested variables did not follow the normal distribution; therefore, Kruskal–Wallis and Wilcoxon tests were performed to find the significant differences between the tested variables. For a better understanding and graphical interpretation of the results, each cultivar was compared to the median value (horizontal line in graphs) using the Wilcoxon test. The Spearman correlation test at the significance level α = 0.05 was used to analyze the relationships between the variables. Descriptive statistics and normality tests were performed using MS Excel with the XLSTAT package [53 ]. Kruskal–Wallis and Wilcoxon tests were performed using RStudio software, version 1.2.5033 (RStudio Inc.: Boston, MA, USA) [54 ].
4
Multivariate Analysis of Research Variables
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All the data obtained were analyzed by the descriptive statistic for minimum and maximum values, arithmetic average, and standard deviation. Then, all the variables were tested for normality. Tested variables followed the Gaussian distribution according to the Kolmogorov-Smirnov test and the Shapiro-Wilk test. The Pearson correlation test at significance level alpha 0.05 was used to analyze the relationships between the variables. No significant differences were removed from the correlation matrices. A multivariate statistical technique, principal component analysis (PCA), was used to find the pattern of similarity of the observations and the variables by displaying them as points on a map. Analysis of variance was performed to find the significant differences between the tested variables. The correlation test and analysis of variance were performed using the Rstudio software (version 1.2.5033, Vienna, Austria) [26], and descriptive statistics, normality tests, and PCA analysis were performed using MS Excel and the XLSTAT package program [27] .
5
Diffuse Astrocytoma: MGMT Status Impact
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We queried the NCDB (2022 submission) to identify patients with diffuse or anaplastic astrocytoma (International Classification of Diseases (ICD)-O-3 codes 9400 or 9401), IDH-mutant (Brain Molecular Markers 1 or 2) or IDH-wildtype (2 or 4) newly diagnosed between January 1, 2010 and December 31 2019 with follow-up through December 31, 2022. Patients were included if they received chemotherapy. Patients were excluded if they had less than one month of follow-up or missing data for age, sex, race, Charlson-Deyo Comorbidity Index, extent of resection, receipt of radiotherapy, or MGMT promoter methylation status.7 -15 All statistical analyses were conducted using the RStudio software Version 1.4.1106 (RStudio, Inc., Boston, Massachusetts). The Kaplan-Meier method with the log-rank test and adjusted Cox proportional hazards regressions models were used to determine the association of MGMT status with OS. Variables that were statistically significant on univariable analysis were included in the multivariable model. Schoenfeld’s test of weighted residuals was utilized to assess proportional hazard assumption in the Cox model. All analyses were performed at the .05 significance level based on two-sided statistical testing.
6
MGMT Promoter Status and Survival in Chemotherapy Patients
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All statistical analyses were conducted using the RStudio software Version 1.4.1106 (RStudio, Inc., Boston, Massachusetts). For our primary analysis, we measured the association between MGMT promoter status and OS in patients who received chemotherapy, adjusted for covariables. Descriptive statistics were generated and stratified by MGMT promoter status. Associations between MGMT promoter status and other clinical variables were determined using the Pearson's chi-square test and the Wilcoxon rank-sum test. Kaplan-Meier estimates were obtained for OS and compared using the log-rank test. Univariable and multivariable Cox proportional hazards regressions were conducted. Variables that were statistically significant on univariable analysis were included in the multivariable model. Schoenfeld's test of weighted residuals was utilized to assess proportional hazard assumptions in the primary analysis. If the proportional hazards assumptions for a variable were violated, the variable was removed and used as a stratification factor in the Cox models. Exploratory analyses were not corrected for multiple hypothesis testing. All analyses were performed at the .05 significance level based on twosided statistical testing. This report follows the STROBE and REMARK reporting guidelines for observational and biomarker studies, respectively.
7
Comparative Analysis of Phytoplasma Infection
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Symptom development was observed in healthy and CY-infected plants of both lines, from the end of the inoculation period to the time of tissue harvest for different analyses, i.e. when plants were ca. 70 day-old. To evaluate the phenotypic differences between the two lines and the differences due to phytoplasma infection, the rosette fresh weight was measured in plants grown under short-day conditions using 8 healthy and 8 CY-infected plants per line. The day before sampling, soil was saturated with water. Rosettes were then cut at the plant collar level and the fresh weight of each biological replicate was measured. Moreover, the length of the floral stalk was measured in plants grown under long-day light conditions (see above), using 8 healthy and 8 CY-infected plants per line. Statistical analyses were performed using RStudio software Version 1.1.456 (RStudio Team 2020 , Boston, MA, USA). The normal distribution was checked with the Shapiro–Wilk test. Significant differences among the group means were determined with a two-way ANOVA and post-hoc comparisons between all groups were made with Tukey’s test with P < 0.05.
8
Statistical Analysis of Experimental Data
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Data analysis was performed with Minitab software, version 19.0 (Minitab LLC, State College, Pennsylvania, United States) and RStudio software, version 1.1.456 (RStudio Inc., Boston, Massachusetts, United States). The Anderson-Darling test was used to check the normality of data. Normally distributed variables are presented as mean±SD. Non-normally distributed variables are presented as medians with interquartile ranges. A 2-sample, independent, unpaired t test or Mann-Whitney U test was used to assess between-group differences. A Cochran-Armitage test for trend was used to evaluate the trend in incidence rates among groups, while the incidence rates between groups were compared with a Fisher exact test. P<0.05 was considered statistically significant.
9
Phytoplasma Infection Impacts on Plant Phenotypes
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Symptom development was observed in healthy and CY-infected plants of both lines, from the end of the inoculation period to the time of tissue harvest for different analyses, i.e. when plants were ca. 70 days old. To evaluate the phenotypic differences between the two lines and the differences due to phytoplasma infection, the rosette fresh weight was measured in plants grown under short-day conditions using 10 healthy and 10 CY-infected plants per line. The day before sampling, soil was saturated with water. Rosettes were then cut at the plant collar level and the fresh weight of each biological replicate was measured. Moreover, the length of the floral stalk was measured in plants grown under long-day light conditions (see above), using 8 healthy and 8 CY-infected plants per line.
Statistical analyses were performed using RStudio software Version 1.1.456 (RStudio Team 2020, Boston, MA). The normal distribution was checked with the Shapiro-Wilk test.
Significant differences among the group means were determined with a two-way ANOVA and post-hoc comparisons between all groups were made with Tukey's test with P<0.05.
Statistical analyses were performed using RStudio software Version 1.1.456 (RStudio Team 2020, Boston, MA). The normal distribution was checked with the Shapiro-Wilk test.
Significant differences among the group means were determined with a two-way ANOVA and post-hoc comparisons between all groups were made with Tukey's test with P<0.05.
10
Survival Analysis of Cancer Treatment
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Descriptive statistics were calculated as a form of proportions and medians. Fisher's exact test was used to compare proportions, and Wilcoxon rank-sum test was used to compare continuous variables. PFS was defined as the interval from the start of treatment to the date of disease progression or death of any cause, and OS as the interval from the start of treatment to the date of death due to any cause. Kaplan-Meier curves and p-values calculated from log-rank tests were used to compare PFS between pre-defined subgroups. Both univariate and multivariate analyses using the Cox proportional hazards regression model were applied to estimate each factor's hazard ratio and corresponding confidence intervals. Two-sided p-values of 0.05 or lower were considered to indicate statistical significance. R studio software, version 1.2.1335, was used to perform all statistical analyses.
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