All analyses were performed using Statistical Analysis System (SAS) 9.4 for Windows (SAS Institute Inc., Cary, NC, United States). Participants’ sociodemographic and professional characteristics and the levels of burnout were described using frequency and percentage. We divided the participants into two groups for the remaining analyses: “no burnout” and “burnout” (mild burnout, moderate burnout, and severe burnout combined). Chi-square tests were conducted to compare the prevalence of burnout across groups based on sociodemographic and professional characteristics. A multivariate analysis was performed using a forward stepwise logistic regression model. All comparisons were two-tailed, and p-values less than 0.05 were considered statistically significant.
Sas 9.4 for windows
Manufactured by SAS Institute
Sourced in United States
SAS 9.4 for Windows is a software product that provides a comprehensive suite of analytics tools for data management, statistical analysis, and reporting. It enables users to access, manipulate, and analyze data from a variety of sources, and to create customized reports and visualizations. The software is designed to run on the Windows operating system.
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Lab products found in correlation
Stata, by StataCorp (4 mentions)
Spss statistics for mac version 24, by IBM (2 mentions)
Prism 6, by GraphPad (2 mentions)
Sas 9, by SAS Institute (2 mentions)
R statistical software version 3, by R Project for Statistical Computing (1 mentions)
R statistical software, by R Project for Statistical Computing (1 mentions)
Stata version 12, by StataCorp (1 mentions)
R version 4, by R Project for Statistical Computing (1 mentions)
Spss statistics 26, by IBM (1 mentions)
Stata 12 for windows, by StataCorp (1 mentions)
Sas enterprise guide client version 7, by SAS Institute (1 mentions)
Prism 5 for windows, by GraphPad (1 mentions)
Spss software, by IBM (1 mentions)
Spss for window, by IBM (1 mentions)
Spss version 24, by IBM (1 mentions)
Sigmaplot 14, by Grafiti LLC (1 mentions)
Infinium methylationepic array, by Illumina (1 mentions)
Bisulfite conversion, by Zymo Research (1 mentions)
Endnote, by Thomson Reuters (1 mentions)
Spss version 24, by SAS Institute (1 mentions)
211 protocols using sas 9.4 for windows
1
Burnout Prevalence and Predictors among Healthcare Professionals
2
Evaluating Pneumococcal Vaccine Safety Signals
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We conducted a performance evaluation to identify which algorithm was more suitable for detecting safety signals for the pneumococcal vaccines. We established a reference standard by reviewing the adverse reaction section of the FDA-approved package inserts and the labeling information approved by the Ministry of Food and Drug Safety of South Korea. AEs listed on the package inserts [24 ] or in the labeling information [25 ] were used to constuct the reference standard. We then constructed a confusion matrix for each algorithm, comparing the detected signals with the reference standard to calculate: (a) accuracy; (b) sensitivity; (c) specificity; (d) positive predicted value (PPV); (e) negative predicted value (NPV); (f) area under the curve (AUC). Detailed formula used for the performance evaluation is explained in the Figure S1 [26 (link),27 (link)].
To account for effect modification by age, we also conducted subgroup analyses for the following subgroups: (1) 19 years old or younger; (2) 19–64 years old (3) 65 years old or older. All statistical analyses were performed using SAS 9.4 for Windows (SAS Institute, Inc., Cary, NC, USA), R Statistical Software version 3.5.1 (R Foundation for Statistical Computing, Vienna, Austria), and TreeScan® software version 1.4. The study protocol was approved by the Sungkyunkwan University Institutional Review Board (No. 2019-09-005).
To account for effect modification by age, we also conducted subgroup analyses for the following subgroups: (1) 19 years old or younger; (2) 19–64 years old (3) 65 years old or older. All statistical analyses were performed using SAS 9.4 for Windows (SAS Institute, Inc., Cary, NC, USA), R Statistical Software version 3.5.1 (R Foundation for Statistical Computing, Vienna, Austria), and TreeScan® software version 1.4. The study protocol was approved by the Sungkyunkwan University Institutional Review Board (No. 2019-09-005).
3
Survival Outcomes in Transplant Cohort
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Descriptive statistics were used to characterize the patient cohort. PFS was measured from the date of transplant to date of disease progression or death, and patients who were alive without disease progression at last follow-up were censored. OS was calculated from date transplant to last known vital status, and patients were censored if they were alive at last follow-up. PFS and OS rates were computed using Kaplan-Meier estimates and compared using the log-rank test with statistical significance value of P < .05. CIR and NRM were estimated using the competing risks method. Univariate and multivariate analyses were performed using Cox proportional hazards regression models to assess for significant predictive risk factors for PFS and OS. We used a cutoff P value of <.1 to include univariate risk factors in multivariate analyses. All other statistical tests used a significance level of 5%. All statistical analyses were performed using SAS 9.4 for Windows (SAS Institute Inc., Cary, NC).
4
Epidemiology of Band Keratopathy in ESRD
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SAS 9.4 for Windows (SAS Institute, Inc., Cary, NC, USA) was used in this study. Pearson chi-square test was used to compare the demographic characteristics and comorbid disorders between the ESRD and control groups. The incidence rate was calculated as the number of band keratopathy cases identified during follow-up divided by the total person-years (PY) for each group by age, sex, and select comorbidities. The Poisson regression analysis was performed to calculate the incidence rate ratio (IRR), which demonstrated the comparison in the risk of developing band keratopathy between the ESRD and control groups. The adjusted hazard ratio (HR) for developing band keratopathy was calculated using Cox proportional hazard regression analysis. Cumulative incidence rates for band keratopathy of ESRD were evaluated by Kaplan–Meier analysis, and differences in cumulative-incidence rate curves were analyzed using the log-rank test. Additionally, we subdivided the patients into three age subgroups for further analysis: <50 years, 50–64 years, and ≥65 years. Data are presented as mean ± standard deviation (SD), and 95% confidence intervals (CIs) are provided when applicable. Statistical significance was defined as P < 0.05. These statistical assessments were performed in consultation with a statistical expert.
5
Survival Analysis of 1-MV Patients
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Differences in baseline characteristics between groups were evaluated using Pearson’s χ2 test for categorical variables. The actuarial survival rate of the two groups was determined using the Kaplan-Meier method, and a log-rank test was used to compare the difference between the two survival curves. The effect of LC on the mortality risk after 1-MV was assessed using a Cox proportional hazards regression model. Covariates included in the Cox model were age, gender, department to which admitted, number of organ failures, and comorbidities. The proportional hazards assumption was verified using plots of natural log transformed (ln) (survival function) vs ln (time). Significance was set at P < 0.05. SAS 9.4 for Windows (SAS Institute, Cary, NC, United States) was used for all analyses.
6
Xonrid® Slows Radiation Dermatitis Progression
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The clinical investigation hypothesis was that Xonrid®, applied together with SOC preemptive treatment (according to MASCC guidelines) would slow down the progression of radiation dermatitis to G2, thus reducing the number of G2 events at the 5th week.
In a previous observational study focusing only on HNC, 38.2% of the patients treated with SOC did not reach G2 ARD at the 5th week [17 (link)]. In our pilot study, always focused on HNC, the proportion of patients treated with Xonrid® and SOC preemptive treatment not reaching G2 ARD at the 5th week was 82.9% [20 (link)]. We started from the assumption that similar proportions would be observed in the present study, leading to an estimate of 36 patients (18 per treatment group) needed to achieve a power of 80%, with α = 0.05. Four more patients were enrolled, considering a 10% dropout rate. In total, 80 patients were enrolled (40 patients with HNC and 40 with BC).
To compare demographic and baseline characteristics between treatment groups, chi-square or t-tests were used for discrete and continuous variable, respectively.
The statistical analyses were performed using SAS 9.4 for Windows (SAS Institute Inc., Cary, NC, USA).
In a previous observational study focusing only on HNC, 38.2% of the patients treated with SOC did not reach G2 ARD at the 5th week [17 (link)]. In our pilot study, always focused on HNC, the proportion of patients treated with Xonrid® and SOC preemptive treatment not reaching G2 ARD at the 5th week was 82.9% [20 (link)]. We started from the assumption that similar proportions would be observed in the present study, leading to an estimate of 36 patients (18 per treatment group) needed to achieve a power of 80%, with α = 0.05. Four more patients were enrolled, considering a 10% dropout rate. In total, 80 patients were enrolled (40 patients with HNC and 40 with BC).
To compare demographic and baseline characteristics between treatment groups, chi-square or t-tests were used for discrete and continuous variable, respectively.
The statistical analyses were performed using SAS 9.4 for Windows (SAS Institute Inc., Cary, NC, USA).
7
Vitamin D and Insulin Resistance
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Statistical analysis was performed using SAS9.4 for Windows (SAS Institute, Cary, NC, USA). Continuous variables were expressed as the mean ± SD. Because the HOMA-IR values were not normally distributed, we used a logarithmic transformation (base 10) to normalize the variable. Comparisons between groups were tested by analysis of variance (ANOVA) for the normally distributed variables. Pearson's correlation was used to analyze the relationship between 25(OH)D and other indexes. Three models were constructed in regression analysis. Model 1 was unadjusted. Model 2 was adjusted for age, smoking, and drinking. Model 3 was additionally adjusted for systolic blood pressure and BMI. All analyses were two-sided, and P < 0.05 was considered significant.
8
Relapse Risk Assessment in Acute Leukemia
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A Fisher exact test was used when comparing categorical variables. Mann-Whitney and Kruskal-Wallis tests were used when comparing numerical variables in two groups or three or more groups, respectively. The cumulative incidence rate of relapse was determined using the competing risk method. The association between an IDH1/2 mutation and the cumulative incidence outcome was determined using a proportional subdistribution hazards regression model (Fine and Gray regression model).30 Differences in the cumulative incidence among patients with different mutations were assessed using the Gray test.31 Time to relapse was calculated from the date of morphological remission to the date of relapse. All variables with a P value <0.05 (two-tailed) were considered to be statistically significant. Statistical analyses were performed using GraphPad Prism 6 (GraphPad Software, Inc., La Jolla, CA, USA) and SAS 9.4 for Windows (SAS Institute Inc., Cary, NC, USA).
9
Analyzing Iso-Acuity Contours and Astigmatism
10
Gastroenterologist Survey on IBD Collaboration
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Response rate was calculated by dividing the number of respondents by the estimated total number of luminal gastroenterologists in Canada to whom the survey was distributed. Characteristics of respondents were summarized using descriptive statistics. Continuous variables were reported as means and SDs, and categorical variables as counts and percentages. Group comparisons were performed between respondents affiliated with collaborative and non-collaborative IBD centers using chi-square test for categorical variables, and t-tests for continuous variables. If assumption of normality were not met, then Mann–Whitney tests were used and median and interquartile range reported. The level of significance was P < .05. Analyses were conducted using SAS 9.4 for Windows (SAS).
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