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R version 1.1.419 is a free and open-source software environment for statistical computing and graphics. It provides a wide variety of statistical and graphical techniques, including linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering, and more. R version 1.1.419 is part of the ongoing development of the R Project for Statistical Computing.

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12 protocols using r version 1

1

Diagnostic Accuracy of MRI Prostate Zones

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Positive and negative predictive values (PPVs, NPVs) as well as sensitivity and specificity for detection of csPCa were computed for each of the terms within each zone. For TZ lesions, PPVs of shape and border terms in combination with DWI/ADC terms were additionally analyzed. PPVs of term combinations were compared with PPVs of single terms using the generalized score test by Leisenring, Alonzo, and Pepe [20 (link)]. Results were declared to be significant if p < 0.05. Statistical evaluation was performed using R version 1.1.419 (www.r-project.org) and Microsoft Excel version 16.16.17.
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2

Diagnostic Accuracy of PI-RADS v2.0 vs v2.1

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Data was analyzed on a per lesion basis and was modeled in terms of a factorial diagnostic trial involving the factors “scoring system” (PI-RADS v2.0 versus PI-RADS v2.1) and “reader”. The area under the ROC curve (AUC) of each reader and scoring system combination assessed the diagnostic accuracy. Since data are measured on an ordinal scale, we applied the nonparametric ANOVA-type statistic17 (link) to test differences between the AUC of the two scoring systems and the three readers as well as interactions between readers and scoring systems18 (link). Proportions of cancerous lesions per score were compared using the two-proportions Z-Test. Sensitivity, specificity, positive predictive value (PPV) and negative predictive value were calculated after dichotomizing the PI-RADS assessment categories using a predefined cut-off value: a PI-RADS category of ≥ 3 was defined as positive. Sensitivity and specificity of the two versions were compared using the McNemar’s test. PPV and NPV were compared using the test by Lange and Brunner19 (link).
Results were declared to be significant if p < 5%. Statistical analysis was performed by F.K. and T.P. using R version 1.1.419 (www.r-project.org) and SAS version 9.4.
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3

Investigating Metabolite Dynamics

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All statistical analyses were performed using R, version 1.4.1717 (The R Foundation for Statistical Computing, Vienna, Austria), including the packages within the tidyverse (tidyr, dplyr, broom, and ggplot2) [23 (link)], ggtext, and emmeans. Continuous variables are reported as geometric means (95% prediction intervals, PI) and categorical variables as counts (percentages). The marginal geometric mean (95% geometric CI) concentration was estimated for each time category from a linear regression model adjusted for sex, age group, and BMI and presented visually as a function of time since last meal. The p-value for time since last meal is presented on the figures. Unadjusted geometric mean metabolite concentrations with 95% geometric CIs at each timepoint were also plotted as a function of time since last meal for the two age cohorts and for males and females separately. To explore the potential effects of sex and age, product terms for time*sex, and time*age groups, were included in the model, and the p-values added to the figures.
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4

Statistical Analysis of ICD Interventions

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For descriptive statistics, continuous data were presented as means with standard deviation (SD) and medians with interquartile ranges (IQR), respectively. Categorical data were presented as proportions. Normality of data distribution was assessed using the Shapiro-Wilk test. Comparisons between groups were performed using the Pearson's Chi-squared test for categorical variables, and Student's t test or Mann-Whitney U test for unpaired continuous variables, and Wilcoxon rank-sum test for paired variables, according to data distribution. Logistic regression models with odds ratios (OR) and 95% confidence intervals (CI) were used to assess the risk of an ICD intervention. A p value of < 0.05 was defined as statistically significant. The statistical software used for data analysis and visualization was R version 1.4.1717 (The R Foundation for Statistical Computing, Vienna, Austria).
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5

Comparative Outcomes of RASopathy Patients

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Continuous variables were tested for normal distribution using the Shapiro-Wilk test. Normally distributed variables are expressed as mean ± standard deviation, and non-normally distributed variables are expressed as median (interquartile range [IQR] or range). Categorical variables are presented as proportions. Statistical significance was defined as P < .05. Kaplan-Meier survival analysis with the log-rank test was used to estimate time-dependent outcomes. Reintervention was assessed with death as a competing risk using the Fine-Gray subdistribution hazard. A subgroup analysis was performed to compare outcomes in patients with and without RASopathy. Statistical analysis was performed using R version 1.4.1106 (R Foundation for Statistical Computing).
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6

Cohort Study of Mental Disorders

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The prevalence of general characteristics was reported, and differences were estimated using the chi-square test. The cumulative incidence rates of mental disorders and suicidal behaviour at the ages of 2 years to 28 years were calculated for both cohorts (NFBC 1986 and the comparison cohort) and the significance of differences was estimated with the chi-square test. Risk ratios (RRs) with 95% confidence intervals (CIs) were calculated by sex separately in each diagnosis group where the number of cases was large enough. Additionally, the Mantel-Haenszel risk ratio (RRMH) was used to calculate the adjusted RRs (adjusted for general characteristics: education, and marital status) in each diagnosis group where the number of cases within each adjustor was large enough. The numbers of suicide attempts within each general characteristic were too low to be analysed with RRMH. The analysis was performed using R version 1.4.1106 (R Foundation for Statistical Computing, Vienna, Austria).
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7

Transitional Support Program Readiness

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We calculated the sample size for a two-group comparison without considering the effect of covariates because data predicting the effect of covariates were unavailable. The mean TRANSITION-Q total score was 52.74 ± 12.40 in patients with childhood-onset chronic diseases aged 12–18 years, who did not receive a transitional support program (16 (link)). In addition, based on a randomized controlled trial of a transitional support program, we estimated that the score in the self-management domain of the Transition Readiness Assessment Questionnaire (TRAQ), a measurement to assess transition readiness that is different from the TRANSITION-Q, increased by approximately 0.7 SD after intervention (19 (link)). A total of 66 participants in both groups were calculated to increase the TRANSITION-Q-J score by 8.7 points (0.7 SD), with a power of 80 and an alpha of 0.05 (two-sided test), using R version 1.37 (R Foundation for Statistical Computing, Vienna, Austria). The dropout rate in previous studies ranged from 5.0 to 11.5% (19 (link), 20 (link)); we predicted a higher dropout rate than previous studies because the design of this study required two visits to the transitional outpatient clinic to participate in the program. Finally, we set the sample size as 40 participants in each group, with a total of 80 participants.
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8

Comparative Analysis of Intervention Outcomes

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R Version 1.37 (R Foundation for Statistical Computing, Vienna, Austria), IBM SPSS 25.0 J for Windows (SPSS, Chicago, IL, United States), and IBM SPSS Amos ver. 25.0 (SPSS, Chicago, IL, United States) were used for analyses, and p < 0.05 (two-tailed) was considered significant. We included data from patients who deviated from the study protocol but did not include the data from patients who dropped out. Descriptive statistics were calculated for the demographic and disease variables. We compared the intervention group and control group using Mann–Whitney’s U test and Fisher’s exact test. For each outcome, we compared the two groups at each time using analysis of covariance (ANCOVA), as the baseline was covariate. In the event of an interaction between the independent variable (group) and covariate (at baseline) by parallelism test, we compared the groups using analysis of variance (ANOVA).
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9

Analyzing Bacterial Species and Antibiotic Resistance in CAUTI

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Group differences in the proportions of bacterial species and antibiotic-resistant isolates from CAUTI and non-CAUTI samples were investigated using the χ2 test (or variants thereof). A 2-sided P value < .05 was considered statistically significant. All data were analyzed using R version 1.3.1073 software (R Foundation for Statistical Computing, Vienna, Austria).
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10

Nursing Home Antibiotic Modification Opportunities

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Fluoroquinolone antibiotic courses for which urine culture data were available and results had been acquired at least 48 hours before the resident was discharged from the facility were considered for review. Cases were excluded if there was not enough information in the medical record, the antibiotic was started in error, the patient was transferred to the hospital prior to completion of therapy, or if a nonfluoroquinolone antibiotic was prescribed. If the available urine culture data indicated an organism susceptible to at least 1 oral nonfluoroquinolone alternative (ie, nitrofurantoin, trimethoprim-sulfamethoxazole, or first-generation oral cephalosporins), the antibiotic was considered potentially amenable to modification.
The proportions of antibiotic courses amenable to each PMO and days of microbiologically active antibiotic duration were calculated. Comparisons of microbiologically active antibiotic durations between nursing home resident groups were performed using t tests. A 2-tailed P value of <.05 was considered statistically significant. Data and statistical analyses were conducted in R version 1.3.1073 software (R Foundation for Statistical Computing, Vienna, Austria).
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