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R version 3.6.1 is a free and open-source software environment for statistical computing and graphics. It is a programming language and software environment for statistical computing and graphics. R version 3.6.1 provides a wide variety of statistical and graphical techniques, including linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering, and others.

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116 protocols using r version 3

Schistosomiasis and Geohelminth Infections: Prevalence and Treatment Outcomes

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Data were entered into the CISA database and then exported to SPSS Statistics for Windows version 20.0 and R version 3.0.1 (IBM, Armonk, NY, USA). Analyses were performed to determine prevalence and intensity reduction, intensity and reinfection rates resulting from Schistosoma haematobium, geohelminths and H. nana infections, 1 and 6 mo after treatment.
Prevalences were calculated as the frequencies of the outcome over the total samples with valid results. The intensity of Schistosoma haematobium infections and of intestinal parasites was recorded according to WHO¹⁵ (Table 1). Also, the age-specific severity of anaemia was defined as recommended by WHO.^{16 (link),17}The prevalence reduction rate (PRR) was calculated as:

\frac{\begin{matrix} % prevalence before the treatment - \\ % prevalence after the treatment \end{matrix}}{% prevalence before treatment} ⁎ 100

The intensity reduction rate (IRR) was calculated as:

\frac{\begin{matrix} Geometric mean of eggs before the treatment - \\ geometric mean eggs after the treatment \end{matrix}}{geometric mean before the treatment} ⁎ 100

The post-treatment reinfection rates were calculated as:

\frac{Number of children who became positive after the treatment}{Number of children who turned negative after the treatment} ⁎ 100

χ² McNemar’s, paired sample t and Wilcoxon related samples tests were used to compare difference in the prevalence and intensity of infections. The threshold for significant level was 0.05.

Lemos M., Fançony C., Moura S., Mirante C., de Sousa P., Barros H., Nery S, & Brito M. (2019). Integrated community-based intervention for urinary schistosomiasis and soil-transmitted helminthiasis in children from Caxito, Angola. International Health, 12(2), 86-94.

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Sepsis Diagnosis: Cellular Kinetics Analysis

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Data were checked for normality using the Shapiro-Wilk test. Continuous variables were then compared using a Mann-Whitney test with a Bonferroni correction for multiple comparisons, an unpaired t-test or a 2-way ANOVA where appropriate. Categorical variables were compared using a Chi-squared test. Logistic regression analyses were conducted to examine the relationships between cellular kinetics at pre-specified sample times (e.g. day 7) and the presence of sepsis. Discriminatory power was assessed through the area under the receiver operator characteristic curve (AUROC). Longitudinal analyses were performed using linear mixed-effects models. Sample day was included in these models as a restricted cubic spline to allow for a flexible non-linear relationship between time and the response variable. Analysis was performed using the statistical software packages SPSS (IBM) and R version 3.0.1 (http://www.r-project.org) together with the Ime4, effects, rms and pROC packages.

Dinsdale R.J., Devi A., Hampson P., Wearn C.M., Bamford A.L., Hazeldine J., Bishop J., Ahmed S., Watson C., Lord J.M., Moiemen N, & Harrison P. (2017). Changes in novel haematological parameters following thermal injury: A prospective observational cohort study. Scientific Reports, 7, 3211.

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Multivariate Analysis of Research Data

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Descriptive statistics, including frequencies and proportions, were computed as appropriate. A scree plot and tabulated eigenvalues from the PCA and a dendrogram from the HCA were generated to identify unique clusters. All aggregate analyses were performed using R version 3.0.1 and SPSS version 19 (IBM Corp).

Kandadai V., Yang H., Jiang L., Yang C.C., Fleisher L, & Winston F.K. (2016). Measuring Health Information Dissemination and Identifying Target Interest Communities on Twitter: Methods Development and Case Study of the @SafetyMD Network. JMIR Research Protocols, 5(2), e50.

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Structural Equation Modeling Analysis

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We conducted the statistical analyses, using R version 3.01 (37 ) for the preliminary analysis and AMOS ver. 19 (IBM Inc.) for the SEM. Table 2 shows the means, SDs, skewnesses, and kurtoses of all the variables. The skewnesses and kurtoses of the variables were in the range representing close to normal distribution (skewness, <2; kurtosis, <7) (38 ). Thus, we used parametric methods for the following analysis. To examine the relationship between each of the measures, we used Pearson product-moment correlation coefficients. We conducted SEM with maximum likelihood estimation to test our hypotheses. We then evaluated the model fit using the chi-squared test, the goodness-of-fit index (GFI), the adjusted goodness-of-fit index (AGFI), and the root mean square error of approximation (RMSEA). We considered the fit of the model to be acceptable when the chi-squared test showed non-significance, the GFI and AGFI were greater than 0.95, and the RMSEA was <0.05 (39 ). We used the Akaike information criterion (AIC) to compare the models. When we derived the final model based on these indices, we analyzed each path of the model using Wald tests.

Suzuki K., Kita Y., Kaga M., Takehara K., Misago C, & Inagaki M. (2016). The Association between Children’s Behavior and Parenting of Caregivers: A Longitudinal Study in Japan. Frontiers in Public Health, 4, 17.

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Serum PIIIP Levels in Single Ventricle

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All data were expressed as mean AE standard deviation. Spearman's rank correlation coefficient was used to assess the intervariable relationships. Serum PIIIP levels, expressed as log-transformed values, were compared between patients in the SV groups and control patients by analysis of variance (ANOVA) followed by the Dunnett ' ¼ arterial oxygen saturation SV ¼ single ventricle levels. Correlation between continuously distributed variables was examined by linear regression analysis. A nonlinear effect of Qp/Qs on PIIIP levels in the BTS/PAB group was tested by the random forests method. 14 All analyses were performed using a commercially available statistical software package (SPSS for Windows, Version 22.0, IBM, New York, NY), except for the random forests analysis, which was carried out with R Version 3.0.1 and the package ''random Forest SRC.''

Sugimoto M., Saiki H., Tamai A., Seki M., Inuzuka R., Masutani S, & Senzaki H. (2016). Ventricular fibrogenesis activity assessed by serum levels of procollagen type III N-terminal amino peptide during the staged Fontan procedure. The Journal of thoracic and cardiovascular surgery, 151(6).

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Estimating Air Change Rates Using CO2 Tracer

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Air change rates (ACRs) were estimated for the break, guest and laundry rooms using CO₂ as a “natural” tracer gas and the decay method [37 , 38 ]. The CO₂ concentration of replacement air was set to the measured outdoor level (399–404 ppm). Multiple decay curves of CO₂ levels were available for each space. We used as many decay curves as possible (at least two curves) for each space, selecting curves that had at least 100 ppm change and that followed (at least roughly) the expected declining exponential trend. ACR estimates were estimated by minimizing residuals (using a nonlinear least-squares estimator) and then averaging among the estimates for each space.
Descriptive statistics (e.g., means, standard deviations) were calculated for each data type. Differences were evaluated using the Mann-Whitney U for two samples and the Kruskal-Wallis H for multiple comparisons, both with two-sided statistical tests and a significance level of 0.05. Associations between ACRs, temperatures, and indoor TTVOC concentrations were quantified using Spearman correlation coefficients. A principal component analysis (PCA) was performed to identify potential VOC sources using data from Hotel 1. Data were analyzed using SPSS (SPSS, Inc., Chicago, Illinois, USA) and R version 3.5.2 (R Core Team (2019)).

Lin N., Rosemberg M.A., Li W., Meza-Wilson E., Godwin C, & Batterman S. (2020). Occupational Exposure and Health Risks of Volatile Organic Compounds (VOCs) of Hotel Housekeepers Field Measurements of Exposure and Health Risks. Indoor air, 31(1), 26-39.

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Statistical Analysis of Survival Outcomes

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The chi-square test or Fisher's exact test was conducted to measure the difference between the training and validating sets and the relationship between clinical data and risk score. Spearman's correlation coefficients were computed to investigate the potential relationship between two groups. Both univariable and multivariable Cox regression analyses were performed using the R package ‘survival’. The Kaplan-Meier survival curve with log-rank test was drawn to demonstrate the relationship between IRlncRNAs and OS or RFS by the R package ‘survival’. The Wilcoxon rank-sum test is a nonparametric statistical test mainly utilized for comparing two groups. The ROC curve was generated to measure the accuracy of survival prediction by the R package ‘survivalROC’. All statistical tests were two-sided, and p < 0.05 was considered statistically significant. All analyses were performed in SPSS version 25.0 (SPSS Inc., Chicago, IL, USA) or R version 3.5.2 (http://www.r-project.org/).

Wu Y., Zhang L., He S., Guan B., He A., Yang K., Gong Y., Li X, & Zhou L. (2020). Identification of immune-related LncRNA for predicting prognosis and immunotherapeutic response in bladder cancer. Aging (Albany NY), 12(22), 23306-23325.

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Immune Landscape and Survival Analysis

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Associations between proportions of 22 TIICs and overall survival (OS) were tested using Cox regression with samples with CIBERSORT p value < 0.05. To investigate whether there were different clusters of immune infiltration associated with prognosis, we conducted hierarchical clustering. We combined the Elbow, Silhouette and used Ward’s method to explore the optimal k number of clusters. Correlations between different subsets of distinct immune cells were analyzed using Pearson correlation. Univariate and multivariate Cox regression analysis was performed to evaluate the prognostic value of 22 TIICs. The log-rank test was performed to assess the OS between 2 groups based on median in Kaplan-Meier plots. A nomogram was established based on the multivariate Cox regression analyses using rms package (https://cran.r-project.org/web/packages/rms/index.html). The performance of the nomogram was assessed using Harrel’s concordance index (C-index) and comparing the predicted and actual probabilities for OS. All analysis was conducted using R version 3.5.2 or SPSS Statistics version 24.0.

Yu S., Wang Y., Hou J., Li W., Wang X., Xiang L., Tan D., Wang W., Jiang L., Claret F.X., Jiao M, & Guo H. (2020). Tumor-infiltrating immune cells in hepatocellular carcinoma: Tregs is correlated with poor overall survival. PLoS ONE, 15(4), e0231003.

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AGGF1 as a Diagnostic and Prognostic Marker

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The statistical analyses and plots were conducted using R (version 3.5.2) and SPSS (version 23.0) software. Differences in AGGF1 mRNA expression levels between adjacent and tumor tissues were assessed by using Wilcoxon signed-rank test, as well as the adjacent and paired tumor tissues. The receiver operating characteristic (ROC) curve was drawn to determine the diagnostic significance of AGGF1. The relationship between AGGF1 and clinical features were analyzed with Wilcoxon or Kruskal-Wallis test. The OS of high and low subgroups were compared via the Kaplan-Meier method based on log-rank tests. Univariate and multivariate Cox regression analysis were performed to verify the association between AGGF1 expression and survival along with other clinical features. P value less than 0.05 was considered statistically significant.

Wang W., Zhu G., Lai S., Guo Y., Yin X., Chen D, & Wen L. (2020). Angiogenic Factor with G Patch and FHA Domains 1 (AGGF1) Acts as Diagnostic Biomarker and Adverse Prognostic Factor of Hepatocellular Carcinoma (HCC): Evidence from Bioinformatic Analysis. Medical Science Monitor : International Medical Journal of Experimental and Clinical Research, 26, e919896-1-e919896-10.

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Latent Class Analysis of Sleep Symptoms

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We used latent class analysis (LCA) on 14 symptom questions and the total score of the Epworth Sleepiness Scale (ESS) categorized as 0–5, 6–10, 11–15 and >15 (total of 15 symptom items). We evaluated different clusters solutions, ranging from 1–10. The best cluster solution was informed by the lowest Bayesian Information Criterion (BIC)^{21 (link)} and the most parsimonious scenario (e.g., lowest number of clusters), and validated by clinical interpretation of resulting clusters. Omnibus testing for sex differences in LCA assignment (i.e., symptom subtypes) were calculated using chi square test for independence. This was followed up with Bonferroni corrected z-tests to compare column proportions. Statistical analyses were conducted using R version 3.5.2 and SPSS version 25.²²

Morris J.L., Mazzotti D.R., Gottlieb D.J, & Hall M.H. (2021). Sex Differences within Symptom Subtypes of Mild Obstructive Sleep Apnea. Sleep medicine, 84, 253-258.

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