Statistical analyses were done using “R Statistical Software 4.0.2 (“R: A language and environment for statistical computing”, www.r-project.org )” for Windows. For the analysis of correlation between altitude of the foci surveyed and seasonal presence/absence of each lymnaeid snail species, a Spearman's correlation was used because of non-Gaussian data, complemented by a Poisson distribution lineal model for slope assessment. A P value less than 0.05 was considered significant.
R statistical software 4
Manufactured by R Project for Statistical Computing
Sourced in United States, Germany, Austria
R is a free and open-source software environment for statistical computing and graphics. It provides a wide variety of statistical and graphical techniques, and is highly extensible. R 4.0.2 is the latest version, released in June 2020, which includes various improvements and bug fixes.
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Lab products found in correlation
8 protocols using r statistical software 4
1
Altitude-Dependent Lymnaeid Snail Occurrence
2
DART-HRMS Metabolomics Data Analysis
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The spectral data were pre-processed and statistically analyzed using the R Statistical Software 4.0.2 and the MetaboAnalyst 5.0 web portal. The ion isotopes were removed and the resultant spectra were aligned using internally developed codes. Afterwards, the data were filtered by mean intensity value and normalized by log10 transformation. The pre-processed DART-HRMS signatures were evaluated by univariate statistical analysis. The DART-HRMS signatures were evaluated by univariate statistical analysis. To this aim, a volcano plot was built to visualize p-values resulting from the nonparametric t test with p-value adjusted by False discovery rate (FDR ) (-log10p) and fold-change (log2FC). The ions with a p-value adj ≤ 0.05 and at least 2-fold change (FC > 2 or FC < 0.5) were considered statistically significant.
3
Evaluating Machine Learning Performance
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Descriptive statistics were reported as I quartile/median/III quartile for continuous variables and percentages (absolute numbers) for categorical variables. In addition, Wilcoxon-type tests were performed for the continuous variables and the Pearson chi-square test, or Fisher exact test, whichever is appropriate, for the categorical variables.
Sensitivity, specificity, accuracy, and the receiving operative characteristic curve (ROC) measures, with interquartile ranges (IQR), were used for model comparison and performance assessment. For the most promising MLT, the variable importance plot was reported together with the ROC curve and the median-balanced accuracy measure within the resampling.
The present analyses were performed using R Statistical Software 4.1.0 (Vienna, Austria) [27 ] with the rms [28 ], caret [29 ], and xgboost [30 ] packages.
Sensitivity, specificity, accuracy, and the receiving operative characteristic curve (ROC) measures, with interquartile ranges (IQR), were used for model comparison and performance assessment. For the most promising MLT, the variable importance plot was reported together with the ROC curve and the median-balanced accuracy measure within the resampling.
The present analyses were performed using R Statistical Software 4.1.0 (Vienna, Austria) [27 ] with the rms [28 ], caret [29 ], and xgboost [30 ] packages.
4
Risk Factors for Gastrointestinal Rebleeding
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For normally distributed data, continuous variables are presented as the mean ± standard deviation (SD) and were analyzed by using Student’s t test. In contrast, continuous variables are presented as the median and interquartile range for abnormally distributed data. The Mann–Whitney U test was performed to analyze the data. Categorical variables were expressed as proportions, and the χ2 test or Fisher’s exact test was used to analyze the data as appropriate.
Variables associated with rebleeding (p < 0.05) were incorporated into the multivariate logistic regression analysis (backward stepwise) to identify the independent risk factors. All results are presented as odds ratios (ORs) and 95% confidence intervals (95% CIs). p < 0.05 was considered statistically significant. Then, a predictive nomogram was constructed based on the outcome of the final multivariate logistic regression analysis (p < 0.05). Receiver operating characteristic (ROC) curves were plotted to assess the predictive ability of the nomogram.
All statistical analyses were performed with IBM SPSS software version 24.0 for Windows (SPSS Inc., Chicago, IL, USA) and R statistical software 4.1.0 (www.r-project.org ). A two-tailed p value < 0.05 was considered statistically significant.
Variables associated with rebleeding (p < 0.05) were incorporated into the multivariate logistic regression analysis (backward stepwise) to identify the independent risk factors. All results are presented as odds ratios (ORs) and 95% confidence intervals (95% CIs). p < 0.05 was considered statistically significant. Then, a predictive nomogram was constructed based on the outcome of the final multivariate logistic regression analysis (p < 0.05). Receiver operating characteristic (ROC) curves were plotted to assess the predictive ability of the nomogram.
All statistical analyses were performed with IBM SPSS software version 24.0 for Windows (SPSS Inc., Chicago, IL, USA) and R statistical software 4.1.0 (
5
Assessing Mental Health Impacts on Healthcare Workers
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Summary statistics (mean, standard deviation) were calculated for all continuous variables (PHQ-9 scores, PSS-10 scores, and PROMIS global mental health T-scores). Categorical variables were summarized by counts and percentages. The survey data was stratified by role (e.g., physician, staff) and field (e.g., medicine, dentistry), and analyzed both on an aggregate level and on a group level. The normality of the data was assessed using Q-Q plots to visualize the normality and Shapiro–Wilk test when the Q-Q plots were not clear. Continuous variables were compared using Wilcoxon Rank Sum Test and Kruskal Wallis test since the normality assumption was not satisfied for some cases. Categorical variables were compared using Chi-square test or Fisher’s exact test when the sample size was less than five. Data obtained from attendees of the 2022 Minnesota State Fair between August 25 and September 5, 2022, using the same PROMIS Global-10 survey tool, served as a local non-healthcare control. All statistical analyses were performed using R Statistical Software 4.2.1 (R Foundation for Statistical Computing, Vienna, Austria) with p-values of less than 0.05 considered statistically significant.
6
Survival Analysis of Bidirectional Cavo-Pulmonary Shunt
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Categorical variables are presented as absolute numbers and percentages. Continuous variables are expressed as medians with interquartile ranges (IQR). An independent Student´s t-test was used to compare normally distributed variables. The Mann-Whitney U test was used for variables that were not normally distributed. Survival after hospital discharge and survival after BCPS was estimated by the Kaplan-Meier method. Risk factors for mortality were identified using uni- and multi-variate Cox regression models. Weight fo age z-score (WAZ) was calculated using the WHO Anthro software 3.2. Data analysis was performed using SPSS version 25.0 for Windows (IBM, Ehningen, Germany) and R statistical software 4.2.1 (R Foundation for Statistical Computing).
7
Propensity Score Matching for Colonoscopy Outcomes
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Descriptive data were reported as median and interquartile range. Differences in categorical variables were analyzed using χ2 test. Continuous variables were analyzed using Mann-Whitney U test. Analyses were performed using R Statistical Software 4.1.0 (The R Foundation for Statistical Computing, Vienna, Austria), P values of < 0.05 indicating statistical significance. To reduce selection bias, one-to-one propensity score matching was performed using the R package “Matchlt”. One-to-one matching was conducted with age, sex, body mass index (BMI), ASA score, BBPS, surgical history, and indication for colonoscopy as covariates using greedy matching with caliper of 0.2. Univariable and multivariable logistic regression analyses were performed to assess independent prognostic factors. The covariates for matching estimation included age, sex, BMI, ASA score, BBPS, previous abdominal surgery, and indication for colonoscopy. Covariate selection for multivariate analysis was based on a P value of < 0.2 in univariable analysis, with a logistic regression model.
8
Statistical Analysis of Metabolic Indices and MACE
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The normality of continuous variables was assessed by quantile-quantile plots. Continuous variables in normal distributions were presented as mean ± SD by the independent Student’s t-test, and variables in non-normal distributions performed as median (interquartile range) by the Mann-Whitney U test. Categorical variables were expressed as counts (percentages). The comparison between groups were examined by the Pearson’s chi-squared test or Fisher’s exact probability test (categorical variables), and ANOVA or the Kruskal–Wallis H test (continuous variables). The Kaplan-Meier method is used to plot the time-survival curve. The unadjusted and adjusted Cox proportional hazards model was used to assess the association between the TG-derived metabolic indices (considered as a continuous variable and categorical variable) and MACE. Multiple confounders including clinically relevant risk factors and statistically significant variables in univariate analysis were adjusted. The results of survival analyses were presented as hazard ratio (HR) and 95% confidence interval (CI). The two-sided significance level was set at P-value < 0.05. All statistical analyses were performed with R statistical software 4.1.0 (R Foundation for Statistical Computing, Vienna, Austria) and IBM SPSS 26.0 (SPSS Inc., IBM, Chicago, IL, USA).
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