After assembling nirmatrelvir and ritonavir exposure and outcome data for the matched cohorts in 2 × 2 tables, we estimated the 28-day risk difference (RD) and the 28-day relative risk (RR) for each study outcome. These parsimonious analyses allowed for the straightforward reporting of RDs and numbers-needed-to-treat but did not account for the possible influence of competing risks. The reasonability of this approach was checked by comparing the RR estimates with hazard ratios estimated using the method of Fine and Gray12 (link) and by estimating outcome-specific hazard functions.13 (link) Both of those methods impose a proportional hazards assumption, which was checked using cumulative sums of Martingale-based residuals.14 (link) Data analysis was conducted in SAS version 7.15 (SAS Institute). We used a 95% CI that excluded the null as our level of statistical significance.
Sas version 7
Manufactured by SAS Institute
Sourced in United States
SAS version 7.1 is a software package designed for statistical analysis, data management, and business intelligence. It provides a comprehensive set of tools for data manipulation, analysis, and reporting. The core function of SAS version 7.1 is to enable users to efficiently manage and analyze large datasets, generate statistical models, and create customized reports.
Automatically generated - may contain errors
Lab products found in correlation
Statistix 10, by SAS Institute (3 mentions)
R version 4, by R Project for Statistical Computing (2 mentions)
Spss statistics 24, by IBM (1 mentions)
Stata mp version 15, by StataCorp (1 mentions)
Statistica software version 7, by StatSoft (1 mentions)
R core team software, by R Project for Statistical Computing (1 mentions)
32 protocols using sas version 7
1
Estimating Relative Risks and Hazard Ratios
2
Mortality Risk Factors in COVID-19 Patients
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The association of clinical characteristics with mortality within 60 days was tested using bivariate statistics. Categorical variables were analyzed with a chi-square test. Continuous variables were analyzed using an analysis of variance test. Time to occurrence of the COVID-19-related death in patients prescribed with ARBs or ACEIs was analyzed using Kaplan-Meier graphs displaying failure functions. Statistical significance was assessed using a log-rank test. Cox proportional hazard models calculated estimates of unadjusted HRs and 95% confidence intervals (CIs) for ARBs or ACEIs. The additional factors included in the models as covariates were age group, sex, and comorbidities including hypertension, type 2 diabetes mellitus, coronary heart diseases, heart failure, stroke, chronic obstructive pulmonary disorder (COPD), cancer, and chronic kidney diseases. We diagnosed the proportional hazard assumption using the Schoenfeld residuals plot. Comparisons were considered statistically significant if two-tailed p values were less than 0.05. All analyses were performed using SAS version 7.15 (SAS Institute Inc., Cary, NC, USA) and R version 4.0.0 (The R Development Core Team, Vienna, Austria).
3
Efficacy of Beetle Spray Regimes
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SAS, version 7.15, was used to record and statistically analyze the data (SAS Institute Inc., Cary, NC, USA). Prior to analysis, the data were examined for homogeneity using the Kolmogorov–Smirnov Test. Analysis of variance (ANOVA) was carried out for the data to determine whether there were differences between the spray regimes. Duncan’s multiple range test was used to determine the significance of differences at alpha ≤ 0.05. The correlation between the air temperature and the abundance of B. rufimanus beetles in the traps was analyzed using the Spearman’s correlation analysis.
4
Effects of MS1 on NAFLD
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Statistical analysis was performed using SAS, version 7.0. Results are expressed as means ± standard deviations. The disease-control, MS1:3-L, MS1:3-H, MS1:5-L, MS1:5-H, and normal-control on NAFLD were compared by one-way analysis of variance (ANOVA). Significant differences between groups were identified by Tukey's tests at P<0.05.
5
Retinal Artery Occlusion Incidence and Mortality
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Incidence of Retinal Artery Occlusion and Related Mortality in Korea, 2005 to 2018 estimated the 95% CIs based on the Poisson distribution. χ 2 analysis was used to compare the observed incidence rates between CRAO and noncentral RAO.
Standardized mortality ratios (SMRs) with 95% CIs were calculated to compare the mortality of patients with RAO to that of the general Korean population. The SMR is a ratio of the deaths of patients with RAO vs the deaths in the general population as captured by Statistics Korea.
Statistical analyses were performed using SAS version 7.0 (SAS Institute). Two-sided P < .05 was considered significant. Data were analyzed from February 9, 2021, to July 30, 2022.
Standardized mortality ratios (SMRs) with 95% CIs were calculated to compare the mortality of patients with RAO to that of the general Korean population. The SMR is a ratio of the deaths of patients with RAO vs the deaths in the general population as captured by Statistics Korea.
Statistical analyses were performed using SAS version 7.0 (SAS Institute). Two-sided P < .05 was considered significant. Data were analyzed from February 9, 2021, to July 30, 2022.
6
Statistical Analysis of Experimental Outcomes
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The statistical analysis was performed using SAS version 7 (SAS Institute; Cary, NC, USA). A sample size of 10 per group was determined using the G power program (power = 0.85 and effect size = 0.50) to test the main effects. The results are expressed as the means ± standard deviations (SDs). Univariate analysis was used to check the normal distribution of variables, and one-way analysis of variance (ANOVA) was used to compare the significance of the groups when the variables were normally distributed. Multiple comparisons were conducted using a Tukey test when one-way ANOVA showed a significant intergroup difference. Statistical significance was accepted for p < 0.05.
7
Statistical Analysis of Experimental Data
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The statistical analysis was performed using SAS version 7 (SAS Institute; Cary, NC, USA) and the R package. The data were expressed as the mean ± standard deviation (SD), and statistical significance was set at p < 0.05. Multiple comparisons were conducted with Tukey’s test when the three groups had statistically significant differences. Visualization of the data was conducted using R-studio and the ggplot2 package.
8
Comparative Animal Neurological Outcomes
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All results were expressed as a mean ± SD. Statistical analyses were performed using SAS version 7 (SAS Institute). All variables exhibited normal distribution in univariate analysis. One-way ANOVA was used to determine animal group effects separately for each time point and each treatment. Differences among groups with a P < 0.05 were considered statistically significant by Tukey’s test.
9
Comparative Statistical Analysis of Interventions
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The statistical analysis was performed using SAS version 7 (SAS Institute; Cary, NC, USA). A sample size of 10 per group was determined using the G power program (power = 0.85 and effect size = 0.50) to test the main effects. Results are expressed as means ± standard deviations (SDs). Univariate analysis was used to analyse normally distributed variables. One-way ANOVA was used to compare groups, and multiple comparisons were conducted with the Tukey test when one-way ANOVA showed a significant intergroup difference. Statistical significance was accepted for p-values < 0.05.
10
Animal Group Effects Analysis
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All results were expressed as the mean ± SD. Statistical analyses were performed using SAS version 7 (SAS Institute, Cary, NC, USA). All variables exhibited a normal distribution in univariate analysis. One-way ANOVA was used to determine the animal group effects separately for each time point and each treatment. If one-way ANOVA showed significant differences among the groups, the significant differences were determined using a Tukey’s test.
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