Continuous data are presented as mean ± standard deviation (normal distribution) or medians (range) (non-normal distribution), and categorical data are presented as n (%). The t test (normal distribution), Mann-Whitney U test (non-normal distribution), and chi-square test (categorical variables) were performed to detect significant differences between groups. We presented the unadjusted results, the results of minimally adjusted analyses, and the results of the fully adjusted analyses. The covariance adjustment was determined by the following principle: when added to this model, the corresponding odds ratio had to change by a minimum of 10% [20 (link)]. To assess the reliability of the results, we performed a sensitivity analysis. When BMI and recurrence showed an evident ratio in the smoothed curve, the likelihood ratio test and bootstrap resampling method were used [21 (link)]. Outcome comparisons among different BMI groups were conducted using the Kaplan-Meier method and log-rank test. The Empower Dataweb System (EmpowerStats.com/dataweb/">http://www.EmpowerStats.com/dataweb/ , X&Y Solutions, Inc., Boston, MA) was used for data input, with all the analyses completed with R software (http://www.R-project.org , The R Foundation) and EmpowerStats (EmpowerStats.com">http://www.EmpowerStats.com , X&Y Solutions, Inc., Boston, MA). A <0.05 P value (two-sided) was considered to indicate statistical significance.
R software
Manufactured by X&Y Solutions
Sourced in United States
R software is a programming language and software environment for statistical computing and graphics. It provides a wide range of statistical and graphical techniques, including linear and nonlinear modeling, classical statistical tests, time-series analysis, clustering, and more. R software is widely used in various fields, including academia, research, and data analysis.
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Lab products found in correlation
6 protocols using r software
1
Analyses of Obesity and Cancer Recurrence
2
Evaluating Corticosteroid Effects on COVID-19
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Categorical variables were presented as counts (percentage). Continuous quantitative variables were presented as mean and standard deviation or median and interquartile range of 25–75%. Comparisons between groups were performed by Kruskal–Wallis-test or Fisher's exact-tests. The effects of corticosteroids were evaluated by different outcome (fever duration after admission, total fever duration, length of hospital stay, CRP recovery time, and imaging recovery time) using Kaplan–Meier analysis. Logistic regression analysis were performed with stratifications by severe pneumonia, refractory pneumonia, inflammatory biomarkers, pulmonary images, and different timing of corticosteroid treatment to evaluate the effects of low-dose corticosteroids on different endpoints, and the 75th percentile were chosen as cutoff points. Results were reported as odds ratio (OR) with 95% confidence interval (CI). Since severity of pneumonia was associated with corticosteroid use, we also performed one to two propensity scores matching to minimize the selection bias. All statistical analyses were performed using Empower R (www.empowerstats.com , X&Y Solutions, Inc., Boston MA, USA) and R software (http://www.T-project.org ). A two-tailed P < 0.05 was considered to be statistically significant in all analyses.
3
Oral Contraceptives and Health Outcomes
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Differences in baseline characteristics between OC users and nonusers were analyzed using χ2 tests for categorical variables. Cox proportional hazards regression analyses were adopted to calculate the crude and adjusted hazard ratios (HRs) and 95% confidence intervals (CIs). The following covariates, including age (smooth), race (White, non-Hispanic vs. other/unknown), marital status (married or living as married vs. never married), education (up to high school vs. some college or post-high school training vs. at least college graduate), smoking status (never vs. ever smoked), HRT (ever vs. never use), and body mass index (BMI; <25 kg/m2 vs. ≥25 kg/m2) were assessed in the adjusted model. Subgroup analysis was performed according to the BMI and HRT use. All analyses were performed by R software (version 3.4.3; http://www.R-project.org ) and Empower (version 2.0; X&Y Solutions, Inc. Boston, MA, USA).
4
Statistical Analysis of Continuous and Categorical Variables
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All data were analyzed by SPSS Statistics software version 25.0 (IBM, Armonk, NY, USA), MedCalc software version 18.2.1 (MedCalc Software bvba, Ostend, Belgium), Empower (R) (www.empowerstats.com (accessed on 10 September 2020), X&Y Solutions, Inc., Boston, MA, USA) and R software (http://www.R-project.org (accessed on 10 September 2020)). Shapiro–Wilk tests were used to determine whether the continuous variables followed a normal distribution. The continuous variables conforming to a normal distribution were expressed as the mean ± standard deviation (SD), while those with a non-normal distribution were expressed as the median (interquartile range [IQR]). The Mann–Whitney U test was used to test the differences between the continuous variables, and the chi-square test was used to compare the differences between the categorical variables. DeLong’s test was used to compare the AUC values of the different prediction models [22 (link),25 (link)]. A p < 0.05 was considered to be a significant difference.
5
Predicting Antiviral Plasma Proteins
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Categorical variables were expressed as frequencies and percentages. The chi-square test was used to compare groups. Continuous variables were tested for normality. Non-normally distributed continuous data were presented as the median (interquartile range). Comparisons among groups were performed using the Kruskal–Wallis test. A logistic regression model was established to predictive AVP. In the model, we introduced clinically and statistically significant variables with P<0.05 from the previous univariate analysis. Forward stepwise regression was used to select variables in the multivariable analysis. Results were reported as odds ratios (ORs) with 95% confidence intervals (CIs). Receiver-operating characteristic curve analysis was used to assess predictors of AVP identified by the multivariable logistic regression model, and each respective area under the curve (AUC) was calculated to evaluate the discriminant ability of the prediction model. All statistical analyses were performed using Empower R (http://www.empowerstats.com , X&Y Solutions, Inc, Boston MA, USA) and R software (http://www.T-project.org ). Two-tailed P values <0.05 were considered statistically significant for all analyses.
6
Survival Analysis of Clinical Outcomes
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C-indices were calculated accurately, and calibration and decision curves were drawn. In addition, survival curves were drawn using the K–M method. Statistical analysis was carried out using SPSS 22.0 (IBM, New York, USA), R software (http://www.r-project.org www.empowerstats.com
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