Sas statistical software package version 9

Manufactured by SAS Institute

Sourced in United States

SAS statistical software package version 9.4 is a comprehensive data analysis and statistical modeling tool. It provides a wide range of capabilities for data management, statistical analysis, visualization, and reporting. The software is designed to handle large and complex datasets, offering advanced statistical techniques and predictive modeling capabilities.

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Lab products found in correlation

Prism 5, by GraphPad (2 mentions) R software version 3, by R Project for Statistical Computing (1 mentions) Spss version 28, by IBM (1 mentions) Stata version 16, by StataCorp (1 mentions)

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SAS version 9.1 statistical software package SAS Statistical Software package for Windows, version 9.4 SAS statistical package version 9.4 SAS statistical software package 9.2 SAS statistical software package, version SAS software package version 9.4 SAS statistical software version 9.4 SAS statistical package 9.4 SAS statistical package, version SAS package version 9.4

49 protocols using sas statistical software package version 9

Islet Product Characteristics and CTO Prediction

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Relationships between pre-IAT islet product characteristics and CTOs were examined using ROC curve analysis. The AUC was calculated from ROC curves generated for each islet product characteristic and these values were evaluated. We analyzed several multivariate models and additionally included age, BMI, and purity to the model. A backward selection was used and we assessed the goodness of the models with the AUC of the ROC curve. In addition, we compared the models using the Akaike information criterion (AIC) values with a smaller AIC value standing for a better model. Statistical significance corresponded to p-values <0.05. All statistical analyses were performed using SAS statistical software package, version 9.3 (SAS Institute Inc., Cary, NC) or GraphPad Prism Version 5.03 (GraphPad Software Inc., La Jolla, CA).

Papas K.K., Bellin M.D., Sutherland D.E., Suszynski T.M., Kitzmann J.P., Avgoustiniatos E.S., Gruessner A.C., Mueller K.R., Beilman G.J., Balamurugan A.N., Loganathan G., Colton C.K., Koulmanda M., Weir G.C., Wilhelm J.J., Qian D., Niland J.C, & Hering B.J. (2015). Islet Oxygen Consumption Rate (OCR) Dose Predicts Insulin Independence in Clinical Islet Autotransplantation. PLoS ONE, 10(8), e0134428.

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Tuberculosis Contact Screening Analysis

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We analyzed the data using the SAS statistical software package version 9.3 (SAS Institute Inc., Cary, NC, USA). A descriptive analysis was performed using Fisher exact test to determine TST- and IGRA-positive proportion of the contact and the control group. We performed a multivariate logistic regression analysis to determine the significant association between clinical and environmental factors and LTBI among contacts. p<0.05 was considered to be statistically significant, and the 95% confidence interval was reported to be appropriate.

Yoon C.G., Kang D.Y., Jung J., Oh S.Y., Lee J.B., Kim M.H., Seo Y, & Kim H.J. (2019). The Infectivity of Pulmonary Tuberculosis in Korean Army Units: Evidence from Outbreak Investigations. Tuberculosis and Respiratory Diseases, 82(4), 298-305.

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Salt Stress Response Indices and PCA

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Salt stress response indices (SSRI) were calculated initially through salt stress response index (ISSRI) for each treatment as the value of a parameter for treatment (Pt) and the value of the same parameter (Pc) at optimum condition (control) (Eq. 1) Then cumulative salt stress response indices (CMSSRI) were calculated by adding all the individual ISSRI for all the 16 measured parameters (Eq. 2)^{14 (link)}

ISSR = Pt / Pc

CMSSRI = Pt / Pc

Means, standard deviations (SD) and standard error (SE) were calculated on Excel. ANOVA followed by Fisher’s protected least significant difference test (P ≤ 0.05) was used for all parameters to determine the significant effects (P < 0.05) of salinity and SA using the SATISTIX 8.1. The standard errors were presented in the figures as error bars. Principal component analysis (PCA) was performed on the correlation matrix of 20 treatments and response variables including all 16 growth and physiological attributes. SAS statistical software package version 9.3 was used to perform Principal components analysis (PCA)⁴⁹. Additionally,

CMSSRI

values were also used during PCA analysis to classify salt-irrigated provided with foliar application of SA.

Ferdosi M.F., Shoaib A., Habib S, & Khan K.A. (2021). Modulation of salt-induced stress impact in Gladiolus grandiflorus L. by exogenous application of salicylic acid. Scientific Reports, 11, 15597.

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Evaluating Ethnic Differences in Soy Intervention Biomarkers

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For all analyses, the SAS statistical software package version 9.3 (SAS Institute Inc., Cary, NC) was used and the intent-to-treat principle was applied. Biomarkers and percent mammographic density were log transformed to meet assumptions of normality. We applied mixed-effects regression (PROC MIXED), which allows for missing values, to examine the effect of the soy intervention in each trial separately while taking into account the covariance structure of the repeated measurements. Based on the assumption that the covariance structure is the same at all points in time (18 (link)), the “compound symmetry” option was selected in all models. To test the first hypothesis, a fixed ethnicity effect in the mixed models evaluated possible differences in biomarker levels between Asian and non-Asian women. To examine potential effect modification by ethnic group during the soy intervention (hypothesis 2), we included an interaction term between ethnicity and the dietary assignment, i.e., low vs. high soy, and stratified the models by ethnicity. In addition, all BEAN2 models were tested for possible order effects resulting from the cross-over design; only the model for NAF volume showed a significant effect (p=0.03).

Maskarinec G., Ju D., Morimoto Y., Franke A.A, & Stanczyk F.Z. (2016). Soy Food Intake and Biomarkers of Breast Cancer Risk: Possible Difference in Asian Women?. Nutrition and cancer, 69(1), 146-153.

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Statistical Analysis of Experimental Data

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Statistical analysis was performed using one-way analysis of variance (ANOVA) and Student’s t-test in the SAS statistical software package version 9.3 (SAS Institute Inc., Cary, NC, United States). Duncan’s multiple range test was performed to compare the differences between means (Harter, 1960 (link)). GraphPad Prism version 5 (GraphPad Software, Inc., San Diego, CA, United States) was used to visualize the data. The level of confidence at which experimental results were considered significant was P < 0.05.

Wang J., Zeng Y., Wang S., Liu H., Zhang D., Zhang W., Wang Y, & Ji H. (2018). Swine-Derived Probiotic Lactobacillus plantarum Inhibits Growth and Adhesion of Enterotoxigenic Escherichia coli and Mediates Host Defense. Frontiers in Microbiology, 9, 1364.

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Effects of Lactobacillus johnsonii L531 on Salmonella-Induced Diarrhea in Piglets

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The SAS statistical software package, version 9.3 (SAS Institute Inc., Cary, NC, USA), and the software’s PROC MIXED procedure were used for statistical analyses. For non-normally distributed and repeated-measure data analysis, the non-parametric Friedman’s test using the SAS procedure FREQ was performed to compare diarrhea scores between treatments. Moreover, the non-parametric Wilcoxon–Mann–Whitney U-test was performed to compare differences between treatments in histologic scores for the small intestine. The statistical significance of differences between groups was determined by two-tailed Student’s t test or one-way analysis of variance. Data are presented as the mean ± standard error of the mean (SEM), except for Figure 1. P-values: *P < 0.05; **P < 0.01; ***P < 0.001.Figure 1

Effect ofL. johnsoniiL531 pretreatment on the occurrence ofS. Infantis–induced diarrhea. Piglets (n = 6 per group) received sterile physiologic saline intragastrically (CN), sterile physiologic saline intragastrically followed by S. Infantis (1.0 × 10¹¹ CFU/mL, 10 mL) challenge (SI), or were pretreated with L. johnsonii L531 (1.0 × 10⁹ CFU/mL, 10 mL once daily) for 1 week followed by S. Infantis challenge (L.j. + SI). Mean values at the same time point without a common superscript (^{a, b}) differ significantly (P < 0.05; Tukey’s test). Bars represent mean ± SD.

Yang G.Y., Xia B., Su J.H., He T., Liu X., Guo L., Zhang S., Zhu Y.H, & Wang J.F. (2020). Anti-inflammatory effects of Lactobacillus johnsonii L531 in a pig model of Salmonella Infantis infection involves modulation of CCR6+ T cell responses and ER stress. Veterinary Research, 51, 26.

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Trends in Metabolic Syndrome Components

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SAS statistical software package version 9.3 (SAS Inc., Cary, NC, USA) was used for data analyses. We analyzed trends in the basic characteristics of study subjects and the prevalence rates of the components of MetS. One-way analysis of variance was used to compare the mean values of continuous variables. Rao-Scott chi-squared test was used to compare categorical variables, including the prevalence of MetS. Fisher's exact test was used to evaluate the trends of the components of MetS from 2008 to 2017. A p-value <0.05 was considered statistically significant.

Park S.I., Suh J., Lee H.S., Song K., Choi Y., Oh J.S., Choi H.S., Kwon A., Kim H.S., Kim J.H, & Chae H.W. (2021). Ten-Year Trends of Metabolic Syndrome Prevalence and Nutrient Intake among Korean Children and Adolescents: A Population-Based Study. Yonsei Medical Journal, 62(4), 344-351.

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Analyzing PCP Density Across Regions

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Statistical analyses were conducted with SAS statistical software package version 9.3 (SAS Institute Inc.).²⁵ A nonparametric 1-way analysis of variance model was used to test for median differences between Medicaid expansion status. To test for differences between regions, a pairwise 2-sided comparison was used as a generalization of the median test. Because the data were stratified by both US Census regions and Medicaid expansion status, a Bonferroni multiple comparisons approach was used to account for the probability that associations would be observed by chance. National and regional analyses were investigated using logistic regressions to model the log-odds of PCP density using maximum likelihood estimation. This procedure also made it possible to examine interaction effects between predictors to determine whether the relationship between PCP density and percentage uninsured differed between regions and Medicaid expansion status. Approximate t tests were performed for the national and regional models to test for differences in slope parameters by Medicaid expansion status.

Hill B.C., Coster D, & Black D.R. (2019). National and Regional Variation in Local Primary Care Physician Density Relative to the Uninsured and the Affordable Care Act. Inquiry: A Journal of Medical Care Organization, Provision and Financing, 56, 0046958019873807.

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Occupational Factors and LTBI

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Data were analyzed using the statistical analysis system (SAS) statistical software package version 9.3 (SAS Institute, Cary, NC, USA). After performing a descriptive analysis using the χ² test, we performed multivariable logistic regressions to identify associations between LTBI and occupational factors. Statistical significance was set at P < 0.05, and 95% confidence intervals (CIs) were reported where appropriate.

Yoon C.G., Oh S.Y., Lee J.B., Kim M.H., Seo Y., Yang J., Bae K.J., Hong S., Yang E.S, & Kim H.J. (2017). Occupational Risk of Latent Tuberculosis Infection in Health Workers of 14 Military Hospitals. Journal of Korean Medical Science, 32(8), 1251-1257.

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Workplace Features and Condom Use

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Due to low levels of missingness (<5%) a listwise deletion approach was taken for missing data. To determine if workplace features and community organization were independently correlated with SWs’ condom use longitudinally, multivariable analyses using generalized estimating questions (GEE) were conducted. A working correlation matrix was also used to help account for repeated measures by the same respondent over three years of follow-up.
A series of confounding models were constructed (one for each subscale, and another for the combined scale) using an approach described by Rothman and Greenland, (29 ) for a total of six models. Confounders were chosen based on a priori knowledge of associations with condom use for pregnancy prevention, and a statistically significant bivariate GEE correlation with our outcome (p<0.20). In addition, variables were also considered confounders if they altered the association of interest by 10%. All potential confounders were included in a full model. Backwards elimination was used to arrive at the final model. SAS statistical software package version 9.3 was used for all data analyses (SAS Institute, Cary, NC, USA).

Duff P., Shoveller J., Dobrer S., Ogilvie G., Montaner J., Chettiar J, & Shannon K. (2015). THE RELATIONSHIP BETWEEN SOCIAL, POLICY AND PHYSICAL VENUE FEATURES AND SOCIAL COHESION ON CONDOM USE FOR PREGNANCY PREVENTION AMONG SEX WORKERS: A SAFER INDOOR WORK ENVIRONMENT SCALE. Journal of epidemiology and community health, 69(7), 666-672.

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