For statistical analysis of the data, we used R software for Windows (version 3.4.3; http://www.R-project.org ). The Shapiro–Wilk test was applied to assess the normal distribution of the data. Comparison of data between groups was performed using one-way analysis of variance when samples were normally distributed or the Kruskal–Wallis test when parametric statistics were not possible. The potential effects of preoperative baseline characteristics on surgical outcomes were analyzed by linear mixed-effects models using the “lem4” package. p values below 0.05 were considered statistically significant.
R software for windows
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R is an open-source software environment for statistical computing and graphics. It is available for Windows operating systems and provides a wide range of statistical and graphical techniques, including linear and nonlinear modeling, classical statistical tests, time-series analysis, clustering, and more.
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20 protocols using r software for windows
1
Statistical Analysis of Surgical Outcomes
2
Predictors of Left Ventricular Ejection Fraction Response
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The χ2 test (categorical variables) and Student's t‐test (continuous variables) were used to determine differences between two groups (HBP and LBBP). McNemar test (categorical variables) and paired Student's t‐test were used for related data. Spearman correlation coefficient was applied to assess correlation between continuous variables. A univariate analysis of seven possible predictive variables of LVEF response at 6‐month follow‐up was performed using a logistic regression. Odds ratio was also computed. The area under the receiver operating characteristic (ROC) curve was calculated for SF correction; the value of SF correction of the ROC curve with the best sensitivity and specificity to discriminate between those with and without echocardiographic response was chosen. Statistical analysis was performed using R software for Windows (R Project for Statistical Computing).
3
Brucella Seroprevalence and Risk Factors
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Categorical variables were presented as numbers and proportions for descriptive statistics. The Chi-square test was used to perform a univariate analysis of categorical variables associated with Brucella seropositivity. The association between seropositivity and exposure to risk factors was reported using an odds ratio (OR) with a 95% confidence interval (CI). All statistical analyses were carried out using R software for Windows (https://www.r-project.org) and GraphPad PRISM version 6.0e (GraphPad Software, San Diego, CA, USA). Every statistical test was two-sided. All statistical procedures were performed with R software for Windows and GraphPad PRISM version 6.0e (GraphPad Software). p < 0.05 was considered statistically significant. All statistical tests were two-sided.
4
Caregiver Stress Management Protocol
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All available data at baseline were analyzed by intention-to-treat analysis. Descriptive statistics (means and percentages) were calculated for caregivers’ and PWAD’s characteristics. Moreover, t tests (or Mann-Whitney tests) and Spearman or polyserial correlations were used to assess associations between variables. The missing data within each scale were treated according to the recommendations of the literature when available. Otherwise, simple mean imputation was used. The last observation carried forward method was used for participants who dropped out. After checking normality and homoscedasticity of primary outcome (PSS-14), we conducted an analysis of covariance (ANCOVA), controlling for regression to mean phenomenon and effects of potential confounders at baseline on primary outcome. All analyses were conducted using R Software for Windows (version 3.0.0).
Interviews and open-ended questions were concurrently analyzed by two trained psychologists (JW and VCL) following the thematic analysis method, using a semantic approach, driven by analytic interests and an essentialist/realist approach [36 (link)].
Interviews and open-ended questions were concurrently analyzed by two trained psychologists (JW and VCL) following the thematic analysis method, using a semantic approach, driven by analytic interests and an essentialist/realist approach [36 (link)].
5
Nomogram for Predicting Microvascular Invasion
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Statistical analyses were performed using the R software for Windows (version 3.4.2, http://www.r-project.org/ ). For the development of the nomograms, we sought outcome predictor from a search of the published work and based on our clinical experience. Logistic regression analysis was used for univariate and multivariate analyses. A final model selection was performed by all subsets regression process with the Mallows Cp statistic. A nomogram was formulated based on the results of multivariate analysis by the package of rms.
We tested the accuracy of the nomograms by discrimination and calibration both in primary and external validation cohorts. The calibration accuracy in predicting the probability of MVI was calculated using Hosmer–Lemeshow test, which assessed whether or not the observed event rates matched the expected event rates in subgroups of the patients. Furthermore, we plotted decision curves to assess the benefits of the nomogram-assisted decisions in a clinical context.
All statistical tests were two-sided, and P values of <.05 were considered as statistically significant.
We tested the accuracy of the nomograms by discrimination and calibration both in primary and external validation cohorts. The calibration accuracy in predicting the probability of MVI was calculated using Hosmer–Lemeshow test, which assessed whether or not the observed event rates matched the expected event rates in subgroups of the patients. Furthermore, we plotted decision curves to assess the benefits of the nomogram-assisted decisions in a clinical context.
All statistical tests were two-sided, and P values of <.05 were considered as statistically significant.
6
Survival Analysis of Treatment Groups
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To evaluate difference between the three groups, ANOVA was used to analyzed continuous variables, and the Pearson χ2 test and Fisher’s exact test were used to compare categorical variables. The survival curves OS and TFS were constructed according to the Kaplan–Meier method with the log-rank test, and the 1-, 2-, 3-, 4-, 5-year survival rates were determined using a life table using the z test. All statistical tests were 2 sides, and P < 0.05 was considered significant. The statistical analyses were performed using the Statistical Package for the Social Science (SPSS) software (version 22.0, SPSS Inc., Chicago, IL, USA) for Windows and R software for Windows (Version 3.6.4 http://www.r-project.org ).
7
Evaluating CNN-Based Histopathology Classification
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The ROC, the AUC, accuracy, specificity (Sp), sensitivity (Sn), positive predictive value, and negative predictive value were calculated to evaluate the performance of the CNNs. For each pathologist, we calculated Cohen’s kappa statistic to evaluate the level of diagnostic agreement between femto-SRH and H&E results. Cohen’s kappa was also calculated for CNN based SRH vs. ground truth (traditional pathology of the same tissue). The McNemar test or Chi-square was applied to compare the diagnostic results. Two-side statistical tests were conducted and the p value was regarded as statistical significance. The analyses were run on SPSS (version 9.0) and R software for Windows (version 3.5.1; http://www.r-project.org ). Figures 1 c; 3 a–d; 4 a–e are representative of twenty independent experiments. Figures 2 a, c–d; 6b ; are representative of five independent experiments. Figure 7b is representative of two independent experiments.
8
Survival Analysis of Patient Cohorts
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Continuous variables were expressed as the mean ± interquartile range (IQR), and categorical variables were expressed as percentages. The chi-square test was used for categorical variables, and analysis of variance (ANOVA) or the Kruskal-Wallis test was applied for continuous variables. The Kaplan-Meier method and log-rank test were used to analyze survival between the four groups of patients. A P value of <0.05 was considered statistically significant. R software for Windows (version 3.6.3; https://www.r-project.org/ ) was used for all statistical analyses.
9
Evaluating LDH Cutoff for Outcome Prediction
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Continuous data were tested for normal distribution using the Kolmogorov-Smirnov method. Continuous data that conformed to a normal distribution are represented by their mean and standard deviation. Continuous data that did not conform to a normal distribution are represented using the median and quarterback distance [Citation20] . Comparisons between normally distributed continuous variables were performed using the chi-square test or Fisher's exact test. Conversely, comparisons between nonnormally distributed continuous variables used the Mann-Whitney U test.
LDH was analyzed by receiver operating characteristic curve and the truncated value of 215.5 U/L was obtained, which was higher than the cutoff value in the ELDH group. Using PSM, 788 patients were matched 1:1 at the LDH cutoff to balance potential covariables between the two groups. Clinically relevant parameters were assessed using univariate analysis to identify potential risk factors associated with outcomes. Variables with p values < 0.10 in univariate analysis were included in multivariate analysis. Differences at p < 0.05 were considered statistically significant. All statistical analyses were performed using SPSS software for Windows (IBM version 25.0; Armonk, USA) and PSM was analyzed using the R Software for Windows (version 4.2.1).
LDH was analyzed by receiver operating characteristic curve and the truncated value of 215.5 U/L was obtained, which was higher than the cutoff value in the ELDH group. Using PSM, 788 patients were matched 1:1 at the LDH cutoff to balance potential covariables between the two groups. Clinically relevant parameters were assessed using univariate analysis to identify potential risk factors associated with outcomes. Variables with p values < 0.10 in univariate analysis were included in multivariate analysis. Differences at p < 0.05 were considered statistically significant. All statistical analyses were performed using SPSS software for Windows (IBM version 25.0; Armonk, USA) and PSM was analyzed using the R Software for Windows (version 4.2.1).
10
Trends of Atmospheric Ammonia Concentrations
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We applied the non-parametric Regional Kendall Test (RKT) to examine overall trends of atmospheric NH 3 concentrations and four driving factors (i.e., temperature, precipitation, synthetic N fertilizer, and livestock manure production) for five regions and the entire US. The five regions were the West, Midwest, Mid-South, Southeast, and Northeast (Fig. 2a). To explore associations of NH 3 with mean monthly temperature (MMT) and precipitation (MMP), we performed a nonparametric Spearman's partial correlation. Trend and correlation analyses were carried out in R software for Windows (version 4.0.3, www.r-project.org/). The RKT was performed using the 'RKT package' with the estimated Theil-Sens slope (Marchetto et al., 2013) (link). The Spearman's partial correlation was performed using the 'ppcor package' (Kim, 2015) (link). , 1, 2, 3, 4, and5) in (a) represent the West, Midwest, Mid-South, Southeast, and Northeast regions of the US, respectively.
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