Sas system for linux

Manufactured by SAS Institute

The SAS System for Linux is a comprehensive software package designed for data analysis and statistical modeling. It provides a robust and reliable platform for processing and managing diverse data sources on the Linux operating system. The core function of the SAS System for Linux is to enable users to perform a wide range of statistical analyses, data management tasks, and business intelligence operations.

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Lab products found in correlation

Sas stat software, by SAS Institute (3 mentions) Sas stat software version 9, by SAS Institute (2 mentions) Sas software, by SAS Institute (2 mentions) Sas stat software version 9.4 of the, by SAS Institute (1 mentions)

6 protocols using sas system for linux

Telehealth Preferences During COVID-19

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We used the American Life Panel response weights to produce nationally representative estimates of answers to the survey questions.¹⁷ We calculated descriptive statistics (counts, means, SEs) and cross-tabulated frequencies and percentages. We used Rao-Scott χ² tests for bivariate comparisons of in-person or video visit preferences by demographic variables (sex, age, race and ethnicity, educational level, family income, urbanicity, and previous use of video visits) and for adjusted logistic regression analyses of binary outcomes of telehealth preferences after the COVID-19 pandemic by demographic variables (sex, age, race and ethnicity, educational level, family income, urbanicity, and previous use of video visits). Missing data were limited (<1% of all variables) and likely random; entries with missing values were dropped from the adjusted analyses.
Analyses were conducted using SAS/STAT software, version 9.4 of the SAS System for Linux (SAS Institute). The threshold for statistical significance was a 2-sided P < .05.

Predmore Z.S., Roth E., Breslau J., Fischer S.H, & Uscher-Pines L. (2021). Assessment of Patient Preferences for Telehealth in Post–COVID-19 Pandemic Health Care. JAMA Network Open, 4(12), e2136405.

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Social Engagement and Mortality Risk

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Descriptive summaries of all variables at study baseline including covariates were conducted and crude associations examined. Cox proportional hazard models examined the relationship between individual measures of late-life social engagement (i.e., social activity, social network size, life space, and purpose in life) and mortality; model assumptions were verified by examining the correlation of the Schoenfeld residuals and the ranked survival time, and the cumulative martingale residuals versus simulated from the null distribution.^{29 ,30 (link)} Models included time from baseline to death or censoring, and an indicator for death versus censored adjusting for baseline age, sex, education, CVD risk factor burden, and depressive symptomatology (Model 1). These models were then augmented with the addition of motor gait performance (fully-adjusted Model 2), added as the final (Model 2) covariate given the robust role physical functioning played in other studies investigating social engagement and mortality regardless of race.^{10 (link),11 (link),14 (link)} Missing data was not imputed, instead models allowed for listwise deletion as relevant. Analyses were programmed in SAS/STAT software, Version 9.4 of the SAS System for Linux (SAS Institute, Cary, NC). Statistical significance was considered using 95% CIs and p<0.05.

He B., Gross M, & Roizman B. (1997). The γ134.5 protein of herpes simplex virus 1 complexes with protein phosphatase 1α to dephosphorylate the α subunit of the eukaryotic translation initiation factor 2 and preclude the shutoff of protein synthesis by double-stranded RNA-activated protein kinase. Proceedings of the National Academy of Sciences of the United States of America, 94(3), 843-848.

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Exploring Financial Fragility and Scam Susceptibility

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Characteristics of older adults with financial fragility were identified using student t-test, Chi-squared test or non-parametric Wilcoxon rank sum test, as appropriate. Multivariable linear regression analyses examined the association of financial fragility with scam susceptibility after controlling for age, sex, education, race and income, where the scam susceptibility rating was the continuous outcome. Robustness of the association was assessed by further controlling for previously identified correlates of scam susceptibility that include global cognition, financial decision making and financial literacy. Additional regression analyses explored factors that may explain the association of financial fragility with scam susceptibility. Racial difference in the association was examined using a regression model with an interaction term as well as a stratified analysis.
The analyses were done using SAS/STAT software, version 9.4 of the SAS System for Linux (SAS Institute, Cary, NC). Statistical significance was determined at α level of 0.05.

Yu L., Mottola G., Barnes L.L., Valdes O., Wilson R.S., Bennett D.A, & Boyle P.A. (2022). Financial fragility and scam susceptibility in community dwelling older adults. Journal of elder abuse & neglect, 34(2), 93-108.

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Literacy and Diabetes Indicators in Older Adults

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We tested bivariate associations between key participant characteristics and diabetes indicators, i.e., hemoglobin A1c and blood glucose levels, using correlations or independent sample t-tests as appropriate. We used linear regression models to examine the associations of literacy – total, health, and financial literacy (separately) – with diabetes indicators (separately for hemoglobin A1c and blood glucose). In these models, each diabetes indicator was the continuous outcome. All models included terms for age, sex, and education. Models were then repeated adding diabetes status (i.e., medication use and/or self-report of diabetes), hypertension status, global cognitive functioning, and depressive symptoms as covariates. We also performed a series of sensitivity analyses; e.g., we excluded individuals with self-reported diabetes and/or medication for diabetes to determine whether the relationship between literacy and diabetes indicators in older adults was independent of diabetes status. Analyses were programed using SAS/STAT software, Version 9.4 of the SAS System for Linux (SAS Institute, Cary, NC).

Lamar M., Wilson R.S., Yu L., James B.D., Stewart C.C., Bennett D.A, & Boyle P. (2018). Associations of literacy with diabetes indicators in older adults. Journal of epidemiology and community health, 73(3), 250-255.

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Decision Making and Blood Sugar

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We first examined bivariate associations between decision making and the two blood sugar indicators, as well as key participant characteristics including demographics (age, sex, education) and other covariates using correlations or independent sample t-tests as appropriate. We then used a series of linear regression models to assess the relationship between decision making, HbA1c, and blood glucose. We first controlled for demographics only, with age and education mean centered. We then added covariates including known diabetes status, hypertensive status, and depressive symptomology. In a secondary analysis, we included the interaction of known diabetes status with decision making to determine whether the observed associations between decision making and blood sugar indicators varied by known diabetes status. As a sensitivity analysis to ensure that findings were not driven by low cognitive function, we excluded individuals scoring at the bottom 10% of our global composite of cognition. For estimates of association we present beta coefficients with 95% confidence intervals and p-values. A one-unit change on the score on the decision making scale corresponds to the estimated change in the modeled blood sugar indicator. All analyses were programmed using SAS/STAT software, Version 9.4 of the SAS System for Linux (SAS Institute, Cary, NC).

Eloesa V., Lamar M., Yu L., Bennett D.A., Barnes L.L, & Boyle P.A. (2022). Decision making and blood sugar indicators in older African American adults. Journal of aging and health, 35(3-4), 221-229.

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Racial Differences in Smoking Risk Perceptions

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All statistical analyses were conducted using SAS software, Version 9.4
of the SAS System for Linux. Copyright © [2002–2012] SAS
Institute Inc. SAS and all other SAS Institute Inc. product or
service names are registered trademarks or trademarks of SAS Institute Inc.,
Cary, NC, USA. Baseline differences in sociodemographic, clinical, and
smoking-related variables by race were assessed using Chi Square
test/Fisher’s Exact tests for categorical variables, and two sample
t-tests/Wilcoxon rank-sum tests for continuous variables. Cronbach’s
alpha was used to assess the internal consistency of the total smoking risk
perception scale and the subscales, separately for black and white participants.
Linear regression models were used to examine the effect of race on risk
perceptions, for lung cancer and SRDs at 12 months; unadjusted and adjusted
analyses (adjusting for common confounders such as age, gender, income,
education, and controlling for smoking status) as well as cognitive constructs
(worry, anxiety, perceived benefits of screening/quitting) were conducted.
Interactions between race and potential confounders were examined in the
combined model. Interactions with p-values (two-sided) less than 0.05 were
considered statistically significant. Models were also fit
separately for white and black participants to calculate
the significance level of the confounders in the subsets.

Perez G.K., Gareen I.F., Sicks J., Lathan C., Carr A., Kumar P., Ponzani C., Hyland K, & Park E.R. (2019). Racial Differences in Smoking-Related Disease Risk Perceptions Among Adults Completing Lung Cancer Screening: Follow-up Results from the ACRIN/NLST Ancillary Study. Journal of racial and ethnic health disparities, 6(4), 676-685.

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