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Version 9

Manufactured by SAS Institute
Sourced in United States

Version 9.4 is the current release of the SAS software platform. It provides a comprehensive set of tools and functionalities for data analysis, statistical modeling, and reporting. The core function of Version 9.4 is to enable users to efficiently manage, analyze, and present data from a variety of sources.

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20 protocols using version 9

1

Pharmacokinetics of Mirabegron in Humans

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Plasma and urine pharmacokinetic parameters were analyzed using SAS software, version 8.2 (studies 1 and 2), version 9.2 (study 3), or version 9.1 (studies 4 and 5) (SAS Institute, Cary, North Carolina). Safety data were evaluated descriptively, and AEs were H. Iitsuka et al.
described in their entirety. All randomized subjects who received at least 1 dose of study drug were included in the safety analyses. Summary statistics were calculated for all pharmacokinetic parameters by dose or food condition. The geometric mean ratio (GMR) (fed/fasting) of C max and AUC and the 90% CI for the GMR were calculated to evaluate the effect of food on the pharmacokinetic parameters of mirabegron.
In studies 2, 3, 4, and 5, the GMR of the C max and AUC for male versus female subjects (with 90% CIs) were estimated to evaluate the effect of sex on pharmacokinetic parameters.
In studies 4 and 5, the GMR of mirabegron C max and AUC 0-τ on the last day versus C max and AUC 0-24 on the first day (with 90% CIs) were estimated to assess accumulation of mirabegron with multiple dosing.
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2

Pharmacokinetic and Pharmacodynamic Analysis

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Pharmacodynamic endpoints and changes from baseline were summarized using descriptive statistics by time point or collection interval and tolvaptan dose using SAS version 9.1 (Trial 202) or version 9.4 (Trial 203) (SAS Institute, Cary, NC). PK descriptive statistics were determined using S-Plus, version 8.2 (TIBCO Software, Inc., Boston, MA). Slopes and 95% confidence intervals (CIs) of plots of individual subject log dose versus Log Cmax or Log AUC were determined in Sigma Plot, version 12.5 (Systat Software, Inc., San Jose, CA) as were correlations between maximal increases in serum sodium, fluid balance, and urine volume.
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3

Longitudinal Analysis of Osteoarthritis Progression

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Linear mixed models were estimated to test for differences in outcomes between classified groups (control vs. stable OA vs. progressive OA) at enrollment (year 0) and year 6 (SAS Institute Inc., Version 9.4, Cary, NC). The rate of change in the outcomes over the 6-year study period were analyzed using the same method. Classical sandwich estimation was used to protect against possible model misspecification. Pairwise comparisons between groups (control vs. stable, control vs. progressive, and stable vs. progressive) were conducted via orthogonal contrasts. The Holm test was used to correct for multiple comparisons. Estimates of baseline and rates of change over time are reported by OA classified group, along with their 95% confidence interval. A P < 0.05 was used to determine statistical significance.
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4

Defoliation Patterns of Wild Soybean Pests

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Among organisms feeding upon wild soybean, Coleopterans, Orthopterans, and Lepidopterans involved in defoliation were consistently observed across all populations within each prefecture allowing for statistical analysis. An analysis of variance (ANOVA) was performed using SAS® version 9.4[43 ]. A factor “Environment” was created by concatenating the site with the year of the observations. The observations within an “Environment” were grouped by each of the three growth stages (“Vegetative”, “Flowering”, and “Pod/Seed development”). If a growth stage within a year contained multiple observations, the average was used for the analysis. A log10 (1+X) variance stabilizing transformation was utilized to satisfy the ANOVA assumptions. Pairwise comparisons between Coleopterans, Orthopterans, and Lepidopterans within each prefecture, year, and growth stage were defined within the ANOVA and tested using t-tests. The level of statistical significance was predetermined to 5% (α = 0.05).
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5

Comparing COVID-19 Vaccine Protocols

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The two active vaccine groups were each compared with the pooled placebo group and analyzed according to the intention-to-treat principle. Statistical analyses were performed with the use of SAS software, version 9.4 (SAS Institute), and R software.4 All P values are two-sided. P values for clinical events were based on Barnard’s test, and those for injection-site reactions and symptoms on Fisher’s exact test. Given the many comparisons with no adjustment for type I error, we recommend caution in interpreting P values between 0.05 and 0.01.
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6

Statistical Analysis of Categorical Data

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Comparison between groups was performed by chi-square or Cochran-Mantel-Haenszel tests. The trend analysis of classification variables was performed by Cochran-Armitage trend test. A p value less than .05 was considered statistically significant. All analyses were conducted using a statistical analysis system software, Version 9.4 (SAS Institute Inc. Cary, USA).
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7

Cost-Effectiveness Analysis of Ferric Citrate in Hemodialysis Patients

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In previous 28-week and 52-week studies of FC in Japanese haemodialysis patients with hyperphosphatemia12 (link), the mean (SD) change in the dose of ESAs from baseline to week 24 was—1045.6 (2579.0) IU/week. The intended sample size was estimated to be 90 patients in total to provide 83.3% power to detect the difference in mean changes in the dose of ESAs from baseline to the EOT between the FC and control groups with a type 1 error of 5% by the Wilcoxon rank sum test. Based on the full analysis set (FAS) data from the ASTRIO study (FC (n = 46), control (n = 45)), we analysed data for drug costs and RDW that were available at both baseline and EOT (FC (n = 40), control (n = 42)) for the cost-effectiveness analysis. Differences in changes in mean drug costs between the two groups were tested using Student’s t tests with a two-sided significance level of 0.05. Differences in changes in mean RDW between the two groups were tested by analysis of covariance with baseline values as covariates at a two-sided significance level of 0.05. We calculated the rate of achievement of an RDW < 15.5% using the FAS data (FC (n = 46), control (n = 45)) and analysed the differences between the groups using Fisher’s exact test at a two-sided significance level of 0.05. Statistical analyses were performed using SAS version 9.3 or version 9.4 (SAS Institute Inc., Cary, NC, USA).
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8

Prevalence Trends of Degenerative Intervertebral Disc

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The annual prevalence of DIP from 2009 to 2015 was calculated using age- and sex-standardized methods based on data regarding the population distribution issued by the Korea National Statistical Office in 2015. Trends in prevalence were estimated using the compound annual growth rate (CAGR) and Cochran-Armitage test (CAT) for DIP over 6 years. The CAGR was used to explore growth in a more precise, annualized manner. The CAGR was estimated with the formula [(ending value/beginning value)(1/no. of years)]−1.15 (link) A null hypothesis in the CAT is the hypothesis of no trend, which would indicate an equal binomial proportion for all levels of the explanatory variable.16 Statistical significance was evaluated by applying two-tailed tests, and p values <0.05 were considered to indicate statistical significance. All statistical analyses were performed using version 9.4 (SAS institute, Cary, NC, USA)
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9

Evaluating Processed Milk Factors

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Data were analyzed using a PROC MIXED statement to conduct ANOVA on a fixed-effect model (version 9.4, SAS Institute Inc., Cary, NC). A Bonferroni least significant difference test was used to determine significant differences (P < 0.05). Independent variables for evaluating processed milk included type of farm, processing treatment, replicates, and farm × treatment interaction. Independent variables for evaluating in vivo data included type of farm, incubation time, farm × incubation time interactions, and replicates.
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10

Longitudinal BMI Change Analysis

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BMI data from baseline and 1, 2 and 3 year follow-ups were used in random intercept, mixed effects growth curve analyses (SAS Inc. version 9.3) to model BMI change, which use maximum likelihood estimation to accommodate missing data (Singer et al., 1996 (link)). We examined empirical growth plots, fit an unconditional means model, fit an unconditional linear growth model and fit unconditional nonlinear models. Linear growth models consistently showed a better fit than higher order polynomials.
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