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32 protocols using r software

1

Biomarker Usage Across Hospitals

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Continuous variables were described as median values with interquartile ranges (IQR), and categorical variables were described as frequencies with percentages. We employed Wilcoxon rank sum tests for continuous variables, and the chi-square tests for categorical variables. We described the distribution of the biomarker use rate among hospitals with testing capability. We used box plots to display variation in the biomarker use rate across capable hospitals. All tests of statistical significance were two-sided, with a p< 0.05 considered statistically significant. Statistical analysis was performed using SAS software (version 9.2, SAS Institute, Cary, NC) and R software (version 3.0.1).
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2

Statistical Analysis of Anaerobic Bacteremia

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The statistical significance of the differences in proportions was tested using the Pearson chi-square or Fisher exact test (when the minimum count in a contingency table was <5). To account for multiple comparisons, we separately applied the Benjamini-Hochberg false discovery rate correction [19 ] for each species. To maintain a false discovery rate <5% for each species, the significance threshold was established. The Cochran-Armitage trend test was used to test for any trend in the incidence (ie, number of anaerobic bacteremia cases divided by the total number of patients who underwent blood culture testing) across the years. The level of significance was set at P < .05. All statistical analyses were performed using R software (version 4.0.5) and JMP Pro (version 13; SAS Institute, Cary, NC, USA).
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3

Comparative Analysis of IBD Cohort

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For the descriptive statistics, patient and clinical characteristics were described using frequency and percentages for categorical variables. Categorical variables were analyzed with the chi-square test. Boxplots with multiple pairwise comparisons (testing the association of FC with disease severity) with the significance level were generated using ggpubr package with t test method and compare means function. Sensitivity and analysis were performed using “OptimalCutpoints” package in R software.
Non-IBD patients were matched to the IBD patients by age and gender using nearest method with the ratio 2:1 to generate the similar pairwise comparison. In this manner, a larger number of controls were collected to ensure appropriate matching of age and gender to the IBD cohort. P values of less than 0.05 were considered statistically significant for the analyses. All analyses were performed using SAS software, version 9.4 (SAS Institute, Cary, NC) and R software, version 3.5.3.
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4

Sugarcane Genotypic Evaluation: Yield Traits

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The obtained phenotypic, quality, and physiological data of 29 sugarcane genotypes and two local check varieties for nine yield-related traits were subjected to descriptive statistical analysis using R software (Release 9.1.3; SAS Institute, Cary, NC, USA). Analysis of variance (ANOVA) was used to determine the variation in traits among the sugarcane genotypes (Aguiar et al., 2018 (link)).
The least significant difference (LSD) test-taking probability level was used to statistically separate the genotypes’ mean values (p <0.001). Using each trait’s mean sum of squares from ANOVA results, the following genetic parameters were estimated (Asante, Adjah & Annan-Afful, 2019 (link)).
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5

Mice Dietary Impact Analysis

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Based on our previous measurements [20 (link)], a total of 20 mice (10 mice per group) were determined to be required in this study. Statistical data were presented as mean ± SE (standard error of the mean). Significant differences (p < 0.05) between group means were determined by Student’s t-test or Mann-Whitney U test. JMP (version 15.0, SAS Institute Inc., Cary, NC, USA) and R software were used for the statistical analyses.
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6

Improving COPD Outcomes with IDM Intervention

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Descriptive data are reported as means ± SD or percentages as appropriate. Comparisons between groups for descriptive summaries were using chi-square tests for categorical variables and independent sample t test or Mann–Whitney U test, as appropriate, for continuous variables. Least-squares means (LSMeans) were used to assess change from baseline in CAT score. The Youden index were used to determine the cutoff values of COPD IDM intervention duration for improving the CAT score. Logistic regression analysis was used to examine the association between IDM intervention duration and improvement in CAT score that achieved MCID thresholds, and the factors associated with CAT MCID improvement. Cumulative incidence curve and Cox proportional hazards models was used to estimate exacerbations COPD exacerbation events, and results were reported as crude and adjusted hazard ratios. The data management, analysis, and visualization were conducted using SAS version 9.4 (SAS Institute Inc., Cary, NC, USA) or R software (version 4.1.0; The Comprehensive R Archive Network: http://cran.r-project.org). A two-tailed P value of 0.05 was considered statistically significant in all analyses.
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7

Geographic Residence and Fruit/Vegetable Consumption

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To investigate the relationship between V&F consumption and rural-urban residence, stratification was defined in 5 different ways using the US Department of Agriculture’s RUCA codes.37 Definitions and classifications of primary and secondary RUCA codes were specified at ZIP-code level.37 The RUCA, version 2.0, package was used in concert with R software (version R, version 4.0.4; The R Project for Statistical Commuting) to assign RUCA based on the US Department of Agriculture and the University of Washington’s Rural Health Research Center suggested coding schemes.38 Data were transferred to an SAS dataset (version 9.4, SAS Institute), and RUCA number-designations were coded by “urban,” “rural,” “large rural city/town,” “small rural town,” and “isolated small rural town” to correspond to RUCA residency categories to facilitate the exploration of different methods of operationalizing geographic residence.39 Five different categorizations (A through E) of urban- and rural-designated areas were constructed, based on the US Department of Agriculture and the University of Washington’s Rural Health Research Center suggested coding schemes.36
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8

Predictors of Death, BPD, and PIVH in Neonates

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Baseline characteristics and primary predictors were compared between patients who had death or BPD and patients who did not, and patients who had severe PIVH and patients who did not using Wilcoxon rank sum test, Pearson’s Chi-squared test, or Fisher’s exact test as applicable. Significant characteristics that differed between groups were entered into a multivariable logistic regression model for each outcome based on a p-value of <0.2 and clinical significance. P-value of less than 0.05 and odds ratio 95% confidence interval not including 1 was considered statistically significant for the analysis. All the analyses were performed using SAS software, version 9.4 (SAS Institute, Cary, NC) and R software, version 4.0.0.
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9

Optimized Statistical Analysis of Experimental Data

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Results were expressed as mean ± standard error of the mean (SEM). All statistical analyses were performed with SAS software version 9.4 (SAS Institute, Cary, NC), and graphs were generated by R software [34 ] and GraphPad Prism 9 [35 ]. Statistical differences between multiple groups were done using one-way ANOVA test followed by Tukey’s or Dunnett’s post-hoc tests (as indicated in figure legends). Statistical differences in tumor growth over time between multiple groups was done using two-way ANOVA (mixed effects) test followed by Tukey’s post-hoc test. Statistical differences in cell proliferation were assessed by simple linear regression test (proliferation independent variable is log scale). A p value of less than 0.05 was considered statistically significant.
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10

Survival Analysis of Hematological Malignancies

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Statistical analyses were performed using Fisher’s exact, Kruskall-Wallis, Mann-Whitney or Wilcoxon tests. Variable importance analysis was performed to rank variables by predictive power according to the Random Forest algorithm. Complete remission (CR) was defined as recovery of morphologically normal BM and normal blood count, with no evidence of extramedullary disease. Event-free survival (EFS) was measured from diagnosis to either relapse, absence of remission after one course of chemotherapy, or death with censure at time of allogeneic HSC transplantation. Overall survival (OS) was measured from the date of diagnosis to the date of death. Data was censored at last follow-up for patients still alive. Probabilities of survival were evaluated with the Kaplan Meier method and differences between distributions were evaluated by the log-rank test. Multivariate Cox regression models were built including all variables with a P value <0.05 in univariate analysis, and D JC-1 value. P values below 0.05 were considered as statistically significant. All statistical analyses were performed using R Software, Statview V5.0 (SAS institute) and GraphPad Prism 9.
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