Sas statistical package version

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The SAS statistical package, version, is a comprehensive software suite designed for advanced data analysis, statistical modeling, and business intelligence. It provides a wide range of tools and functionalities for data management, manipulation, and analysis. The core function of the SAS statistical package is to enable users to efficiently organize, analyze, and interpret complex data sets, supporting informed decision-making across various industries and research domains.

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SAS statistical package version 9.4 SAS/STAT statistical software package, version SAS version 8.2 statistical package SAS statistical package version 8.1 SAS statistical software package version 8.2 SAS Statistical Software package for Windows, version 9.4 SAS statistical software package, version SAS) version 9.1 statistical package SAS statistical software package version 6.12 SAS statistical software package version 7.1

6 protocols using sas statistical package version

Intracuff Pressure and Postoperative Sore Throat

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All statistical analyses were performed using the SAS® statistical package, version 9.4 (SAS Institute, Cary, NC, USA). Data are expressed as the mean ± SD or as frequencies and proportions, as appropriate. The intergroup differences were assessed using Fisher’s exact test and the Mann–Whitney U-test. Possible risk factors for POST were examined by univariate and multivariate analyses. A receiver operating characteristic (ROC) curve was constructed to investigate the cut-off intracuff pressure to correctly predict POST at the maximum area under the curve (AUC), which ranges from 0.5 to 1.0. Spearman’s correlation coefficient was calculated for intracuff pressure ≥ 17 cmH₂0 at intubation and emergence with cough and hoarseness. A P-value < 0.05 was considered statistically significant.

Lee J.Y., Sim W.S., Kim E.S., Lee S.M., Kim D.K., Na Y.R., Park D, & Park H.J. (2017). Incidence and risk factors of postoperative sore throat after endotracheal intubation in Korean patients. The Journal of International Medical Research, 45(2), 744-752.

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TSH Levels and All-Cause Mortality

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Variables are reported as means (SDs) or as percentages. TSH values were
found to be not normally distributed, therefore we introduced the TSH in the
regression analysis after its log transformation. To test the nonlinearity
relationship between TSH and all-cause mortality, we introduced a quadratic term
of Log (TSH) in the regression analysis. Given the departure from linearity of
the relationship between TSH and all-cause mortality, the cox proportional
hazards models were used to examine the relationship between quartiles of plasma
concentrations of TSH and all-cause mortality over 9 years of follow-up.
Characteristics of subjects were compared across quartiles of plasma
concentrations of TSH using parametric (ANOVA) and non-parametric (Wilcoxon
signed-rank test) test as appropriate. Multivariable Cox proportional hazards
model, adjusted for age, sex and other variables that were significant in the
univariate analyses was used to evaluate the relationship between hormonal
status and mortality. All analyses were performed by the SAS statistical
package, version 9.1 (SAS Institute Inc, Cary, North Carolina) with a type I
error of 0.05

Ceresini G., Marina M., Lauretani F., Maggio M., Bandinelli S., Ceda G.P, & Ferrucci L. (2016). Relationship Between Circulating Concentrations of Thyrotropin, Free Thyroxine, and Free Triiodothyronine and 9-Year Mortality in Euthyroid Elderly Subjects. Journal of the American Geriatrics Society, 64(3), 553-560.

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Comparison of Early and Late H. pylori Eradication

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Demographic data were expressed as categorical data and mean±standard deviation. The data for categorical variables are presented as percentages. We compared the differences between the early and late H. pylori eradication therapy groups using a chi-square analysis. We calculated the hazard ratios (HR) based on a 95% confidence interval (CI) using a multivariate Cox regression analysis to compare the risk of rehospitalization for complicated recurrent peptic ulcers between the early and late H. pylori eradication therapy groups. A p-value less than 0.05 was considered to indicate a statistically significant relationship. All statistical analyses were performed using the SAS statistical package version 9.2 (SAS Institute, Cary, NC, USA).

Chang S.S, & Hu H.Y. (2014). Helicobacter pylori Eradication within 120 Days Is Associated with Decreased Complicated Recurrent Peptic Ulcers in Peptic Ulcer Bleeding Patients. Gut and Liver, 9(3), 346-352.

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Analyzing Income Changes After Industrial Accidents

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We used the t-test and analysis of variance (ANOVA) to compare the general characteristics of the study population and the average incomes before industrial accidents according to the industrial classification (manufacturing, construction, or service). A repeated measures ANOVA was used to compare annual differential incomes before and after the industrial accident, as well as the annual income before the industrial accident and the average annual income 5 years after the accident. A paired t-test and repeated measures ANOVA were conducted to compare the income before the accident and the average annual income 5 years later according to the general characteristics of the study population. To investigate the changes in income from before to after the industrial accident according to industry and RTW status, we set the dependent variable as the income minus the income before the accident from the five-year average income and performed a linear mixed model analysis. All statistical analyses were conducted using the SAS statistical package, version 9.4 (SAS Institute, Cary, NC, USA).

Bae S.W., Oh S.S., Park W.M., Roh J, & Won J.U. (2019). Changes in Income after an Industrial Accident According to Industry and Return-to-Work Status. International Journal of Environmental Research and Public Health, 16(14), 2603.

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Alcohol Treatment Disparities by Race

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First, weighted percentages were computed for select participant demographic and psychiatric disorders correlates by race/ethnicity and treatment status. Second, to estimate prevalence ratios (PRs) for the association between alcohol-related treatment and race/ethnicity, we used a multivariable model adjusting for all other sociodemographic factors/psychiatric covariates. Third, to estimate race/ethnicity-stratified PRs for the association between alcohol-related treatment and sociodemographic factors/psychiatric comorbid disorders, we used two multivariable models, one for each race. In a cross-sectional study design, the odds ratio is also referred to as the prevalence odds ratio. Weighted PRs and 95% confidence intervals (CIs) were directly estimated using PROC GENMOD with Poisson distribution for convergence, log link function and estimate statements for all correlates of seeking treatment or help for AUD, adjusting for effects of all other variables (Thompson et al., 1998 (link)). To account for the NESARC-III complex sample design, all analyses presented were generated with PROC SURVEYFREQ and PROC GENMOD using STRATA, CLUSTER, and WEIGHT statements for appropriate variance and standard error estimates. All analyses were conducted with SAS statistical package, version 9.3, (SAS Institute, Inc., Cary, North Carolina).

Emerson M.A., Moore R.S, & Caetano R. (2018). Correlates of alcohol-related treatment among American Indians/Alaska Natives with lifetime alcohol use disorder.. Alcoholism, clinical and experimental research, 43(1), 115-122.

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Evaluating Lactation Responses in Dairy Cows

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A replicated (4 × 4) Latin Square experimental design, with four treatments and four periods, was applied. Animals were considered experimental units, they were distributed in the square considering its previous milk yield and parity order. Data were submitted to analysis of variance (ANOVA) and regression (linear and quadratic) using the MIXED procedure and considering animal as random effect in SAS statistical package, version 9.0 (SAS, 2002) . The following causes of variation were evaluated: treatment, period, square, cow within square and the interaction square × treatment. The model applied for ANOVA was: Y ijkl = + Q i + T j + P k + QT ij + A (i)l + e ijk . Where: Y ijkl = mean value obtained for each observation; = general mean of the variable in the experiment; Q i = effect of the square, where i = 1 and 2; T j = effect of treatment j, with j = 1, 2, 3 and 4; P k = effect of period, with k = l, 2, 3 and 4; QT ij = interaction between square i and treatment j; A (i)l = effect of cow l within square i; e ijk = experimental error.

Scheibler R.B., Schafhäuser J., Rizzo F.A., Nörnberg J.L., Vargas D.P., Silva J.L.S., Fluck A.C, & Fioreze V.I. (2015). Replacement of corn grain by brown rice grain in dairy cow rations: Nutritional and productive effects. Animal Feed Science and Technology., 208.

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