Time between resident and employee testing was calculated as the days elapsed between first testing dates for each group at a SNF. For sites with multiple testing dates for employees, residents, or both, tests from all dates for a given group were combined to calculate the prevalence at each site. All data analysis was conducted in the R statistical language (R Foundation for Statistical Computing, Vienna, Austria). Frequencies were tabulated for social and demographic data. To test the association between residents and employees who tested positive for SARS-CoV-2, a two-tailed McNemar’s test was used. p values < 0.05 were considered statistically significant.
R statistical language
Manufactured by R Project for Statistical Computing
Sourced in Austria
R is an open-source programming language and software environment for statistical computing and graphics. It provides a wide range of statistical and graphical techniques, and is widely used in various fields for data analysis, visualization, and statistical modeling.
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Lab products found in correlation
27 protocols using r statistical language
1
Time Between Resident and Employee COVID-19 Testing
2
Parasitic Density Analysis with Statistics
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Aggregated parasite density by zones or years was expressed as the geometric mean and median. The comparisons of medians of parasite densities were performed with Mood’s median test and illustrated with violin plot. Proportions were compared using the Chi square test or Fisher’s exact test depending on the sample size. The level of statistical significance was set at P < 0.05. Statistical tests were performed using SigmaStat 3.5 (Systat Software, Inc., Point Richmond, CA) and R statistical language (R Foundation for Statistical Computing, Vienna, Austria).
3
Competing Risks Analysis of Radiation Therapy
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Follow-up was calculated from the date of commencing radiation therapy. Descriptive data were expressed as a median value with associated 95% confidence interval (95%CI) based on the range from the 2.5th to the 97.5th centile values. Median potential follow-up was estimated using the methods of Schemper and Smith.10 (link) A competing risks framework was used for all analyses using a subdistribution weighting method.11 (link) DPC and DOC were treated as independent competing causes of death. First order interactions and nonlinearity of continuous covariable effects were examined. Discrimination assessment was performed using the c-index on both univariables and multivariable models of age, ACE-27 score, and PMN. The c-index is analogous to the area under a receiver operating characteristics curve, with a value of 1 indicating perfect discriminatory ability, and 0.5 indicating pure random chance. All analyses were performed using the R statistical language (R Foundation for Statistical Computing, Vienna, Austria) and estimated P values were two-sided and considered significant at a ≤ 0.05 level.
4
Defining Dengue Warning Signs: Healthcare Perspectives
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Percentages of doctors and nurses choosing a specific answer for a question involving general knowledge or warning signs were obtained. Chi-squared test was performed to compare proportions of categorical variables. A p-value less than 0.05 was considered statistically significant. Multivariable logistic regression analysis was then used to assess the impact of the profession (physician or nurse), country of participants (Vietnam or Egypt or Pakistan), prior training on dengue infection, as well as the frequency of treating dengue patients on participants’ definition of warning signs. The numbers and proportions of the doctors and nurses using either one, two, three, four, or more than five warning signs of the WHO 2009 classification were also calculated to show the variations of healthcare workers defining these signs. Statistical analysis was then conducted using R Statistical Language (R Foundation for Statistical Computing, Vienna, Austria).
5
Statistical Analysis in R
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All statistical analyses were performed with the R statistical language (R Foundation for Statistical Computing, Austria, Version 3.4.2). A test statistic (p-value) less than 0.05 was considered significant.
6
Benznidazole Dose Optimization in Chagas Disease
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Individual benznidazole steady-state concentrations (Css) were derived from the classical Css equation:
Css = F× (Dose×WT.ind)/(CL.ind×tau)
where F is the bioavailability of orally administered benznidazole (set at 100% for simplicity), Dose is the daily benznidazole dose, set at 7 mg/kg/day, WT.ind is the individual patient weight, CL.ind is the estimated individual (Bayesian) benznidazole clearance rate (in L/day) as per POPPK analysis, and tau is the dosing interval (set to 1 in this case, as the dose is expressed in mg/day).
All statistical calculations were performed in R statistical language, V.2.14.1 R Foundation for Statistical Computing, Vienna, Austria, (www.R-project.org ). Diagnostic graphs were produced in R, with the package Xpose 4.[24] (link)
Css = F× (Dose×WT.ind)/(CL.ind×tau)
where F is the bioavailability of orally administered benznidazole (set at 100% for simplicity), Dose is the daily benznidazole dose, set at 7 mg/kg/day, WT.ind is the individual patient weight, CL.ind is the estimated individual (Bayesian) benznidazole clearance rate (in L/day) as per POPPK analysis, and tau is the dosing interval (set to 1 in this case, as the dose is expressed in mg/day).
All statistical calculations were performed in R statistical language, V.2.14.1 R Foundation for Statistical Computing, Vienna, Austria, (
7
Modeling Methane Emissions in Ruminant Livestock
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All data analyses were carried out using R Statistical language (version 4.1.2 (1 November 2021, R Foundation for Statistical Computing, Vienna, Austria)) in RStudio version 2022.7.1.554 [75 ]. The data were analyzed using a linear mixed model fitted with lmer (lme4 package) [76 (link)] using this model:
where Y denotes the expected outcome of the dependent variables of CH4 production (g/d), CH4 yield (g/kg DMI), or CH4 intensity (g/kg ECM). denotes the fixed effect of the random intercept, X1 to Xn denote the fixed effects of the independent variables, β1 to βn denote their corresponding slopes, Si denotes the random effect of the studies, and eij denotes the random error.
where Y denotes the expected outcome of the dependent variables of CH4 production (g/d), CH4 yield (g/kg DMI), or CH4 intensity (g/kg ECM). denotes the fixed effect of the random intercept, X1 to Xn denote the fixed effects of the independent variables, β1 to βn denote their corresponding slopes, Si denotes the random effect of the studies, and eij denotes the random error.
8
Attitudes Towards Systematic Reviews and Meta-Analyses
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Data were collected onto a Microsoft Excel spreadsheet and analyzed using the R Statistical Language (R Foundation for Statistical Computing, Vienna, Austria). Descriptive statistics, including frequencies and percentages in addition to means and standard deviations, were computed to describe respondents’ characteristics and responses. Univariate logistic regression models were used to test associations with the professional background of the respondent (experience in conducting SR/MAs for more than 5 years, having more than fourteen published SR/MA papers, and having a SR/MA paper published in a journal with a journal impact factor (JIF) more than 10) as well as with practical attitudes to literature search, data extraction, and MA, with odds ratios (ORs) and 95% confidence interval (CIs) being presented. A two-tailed P value < 0.05 was used to define a significant correlation.
9
Predictive Modeling for Risk Analysis
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All data were analyzed only in the dedicated server space provided by KNHIS according to the policy on preventing the leakage of public data. Categorical variables are expressed as numbers and percentages, while continuous variables are expressed as means and standard deviations. The receiver operating characteristic curve analysis was used to compare the predictive performances of the prediction model using machine learning techniques and other risk scores. The significance level of statistical analysis was considered p < 0.05. All statistical analyses and machine learning modeling were performed with the R statistical language (R version 3·5·1, R Foundation for Statistical Computing, Vienna, Austria).
10
Cytotoxicity and Intracellular pH Analysis
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Statistical analysis was performed using Excel 2016 (Microsoft) and R statistical language (R Foundation for Statistical Computing, Vienna, Austria). Outliers were excluded by Nalimov outlier analysis from both in vitro and sensory analysis. Effects on cellular viability of the test compounds on HGT-1 cells compared to non-treated cells were excluded by means of a two-tailed Student’s t-test and considered to be significant at p < 0.05. Significant differences in the data set of the intracellular pH were evaluated using a one-way ANOVA with Tukey’s range test for multiple comparisons. At least three biological replicates and two technical replicates were analyzed for each cell culture experiment. Data reported in the results section are stated as mean ± SEM, unless indicated otherwise.
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