Sas software version 9.4

Manufactured by IBM

SAS software (version 9.4) is a comprehensive suite of applications for data management, analysis, and reporting. It provides a range of tools and functionalities for processing, analyzing, and visualizing data from various sources. The software is designed to handle large and complex datasets, offering advanced statistical capabilities and data manipulation features.

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Lab products found in correlation

Spss version 16, by IBM (1 mentions) Simca p 14, by Sartorius (1 mentions) Spss software version 24, by IBM (1 mentions)

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SAS 9.4 (137 citations) SAS version 9.4 (76 citations) SAS software (11 citations)

2 protocols using sas software version 9.4

Oxylipin Metabolite Profiling

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Quantified data were expressed as mean ± SD. Statistical comparisons between sample groups and histoclinical parameters were calculated using unpaired Student’s t-test or ANOVA with post hoc Tukey and Dunnett’s test, where applicable, by SAS software (version 9.4) or SPSS (version 16.0). Multivariate partial least square-discriminant analysis (PLS-DA) was carried out using SIMCA-P 14.0 software (Umetrics, Umea, Sweden). The robustness of the classifier models was validated in a built-in iterative seven-fold leave-one-out approach. Oxylipin metabolites consistently detected in at least 80% of samples were included in the analyses. All known artifact peaks were excluded.

Apaya M.K., Hsiao P.W., Yang Y.C, & Shyur L.F. (2020). Deregulating the CYP2C19/Epoxy-Eicosatrienoic Acid-Associated FABP4/FABP5 Signaling Network as a Therapeutic Approach for Metastatic Triple-Negative Breast Cancer. Cancers, 12(1), 199.

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Racial Disparities in Physical Activity

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The demographic characteristics of the participants were stratified by racial group, and descriptive statistics were used to evaluate possible differences in demographic variables. Multivariable logistic regressions were used to examine the associations among race, physical activity, and SDH factors; all the analyses were weighted. The modeling was executed in two hierarchical steps: the first model evaluated the association between race and physical activity after controlling for variables such as age, gender, marital status, and body mass index (BMI). The SDH factors were added in the second step to evaluate the effects of SDH on differences in physical activity between racial/ethnic groups. All analyses applied two-sided tests using a cutoff of p ≤ .05 for significance. Statistical analyses were performed using SAS software version 9.4 and SPSS software version 24.

Asare M., McIntosh S., Culakova E., Alio A., Umstattd Meyer M.R., Kleckner A.S., Adunlin G., Kleckner I.R., Ylitalo K.R, & Kamen C.S. (2019). Assessing Physical Activity Behavior of Cancer Survivors by Race and Social Determinants of Health. International quarterly of community health education, 40(1), 7-16.

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