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Sas stat statistical software package version

Manufactured by SAS Institute

SAS/STAT is a statistical software package developed by SAS Institute. It provides a comprehensive set of tools for statistical analysis, modeling, and data exploration. SAS/STAT offers a wide range of statistical procedures, including regression, analysis of variance, multivariate analysis, survival analysis, and time series analysis. The software is designed to handle large and complex data sets, and it is widely used in various industries, including healthcare, finance, and academia.

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Lab products found in correlation

2 protocols using sas stat statistical software package version

1

Incidence Rates and Risk Factors for Celiac Disease

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All statistical analyses were conducted using the SAS/STAT statistical software package, version 9.1 (SAS Institute Inc., Cary, NC). Average annual incidence was calculated overall and for the sex-specific, race/ethnicity-specific, and age-specific groups.19 Incidence rates and confidence intervals were estimated assuming a Poisson distribution of cases within the Kaiser Permanente population. Direct standardization of rates for age, race/ethnicity, and sex used the 2000 U.S. census population. Incidence rates were presented per 100,000 person-years.
Behaviors and antecedent diagnoses were compared between CD cases and their interviewed controls (1:1) or EMR control (1:10). Adjusted odds ratios (ORs) and 95% confidence intervals (CIs) were calculated adjusting for birth year, sex, membership length, facility, and zip code (as a proxy for socioeconomic status).
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2

Smoking Cessation Intervention Evaluation

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All statistical analyses were conducted using the SAS/STAT statistical software package, version 9.2 (SAS Institute Inc). We compared the study cases (quitters) and controls (continued smokers) to participants enrolled in the ACRIN-NLST trial on sociodemographic characteristics, medical history, nicotine addiction, and screening results. Rates of 5A delivery were computed for each of the 5 years after smokers' initial screen. Descriptive statistics compared cases and controls on each 5A delivery. Conditional logistic regression models examined the effect of each of the 5As and the covariates (race, highest level of education completed, marital status, positive medical history, screening result, quit smoking motivation, and FTND score) on the odds of quitting smoking.
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