A dichotomous outcome variable was created to measure the presence of a policy supporting a tobacco-free workplace based on responses to “Does your worksite have any written policies in place supporting a tobacco-free workplace?” Demographic variables were presented as frequencies and proportions at univariate level. Bivariate chi-square analyses were conducted to compare the outcome variable by the community sizes of each worksite, number of employees at each worksite, industry classifications, gender composition, ethnic composition, and age distribution at each worksite. A logistic regression analysis was conducted to identify predictors associated with having a tobacco-free policy at the worksite. All variables that were significant at bivariate level were selected as possible predictor candidates for the logistic regression. Interactions between predictors were assessed for significance before a decision was made to include those interactions in the logistic regression model. All statistical analyses were conducted using the SAS software for Windows version 9.3 (Cary, NC). All tests were two-sided, and a p-value < 0.05 was considered statistically significant.
Sas software for windows version 9
Manufactured by SAS Institute
Sourced in United States
SAS software for Windows, version 9.4, is a comprehensive statistical analysis and data management software package. It provides advanced analytical capabilities, data manipulation tools, and reporting functionalities for a wide range of applications. The software is designed to work on the Windows operating system.
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Lab products found in correlation
50 protocols using sas software for windows version 9
1
Predictors of Tobacco-Free Workplace Policies
2
Oseltamivir Prescription Timing Impact
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Based on dates with influenza diagnosed and oseltamivir prescribed, patients were divided into “in-time” and “lag-time” cohorts, by the study year of 2005, 2009, and 2010. To determine whether patient characteristics were similar between the 2 prescription times for oseltamivir, we compared distributions of sex and age. The 2-week follow-up period-specific frequencies of outpatient visit, emergency use, hospitalization, and death were calculated for oseltamivir users in the in-time cohort and the lag-time cohort by study year. We used the Mantel-Haenszel method to calculate the in-time cohort to lag-time cohort odds ratio (OR) and 95% confidence interval (CI) of each event. We further used the multivariable logistic regression model to estimate the ORs of repeat outpatient visits, hospitalization, and death associated with in-time and lag-time treatments by age and sex in all 3 years combined. Similar data analysis method was also performed to estimate ORs of all events combined for 2009 and 2010 to evaluate whether the treatment effectiveness changed in 2010. The SAS software for Windows, Version 9.3 (SAS Institute Inc., Cary, NC) was used for data analyses with the 2-sided P value of 0.05 considered statistically significant.
3
Ventriculitis Evaluation and Statistical Analysis
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All statistical analyses were conducted using the SAS software for Windows version 9.3 (Cary, NC). Descriptive statistics were presented as means and standard deviations for continuous variables (for example, age), and frequencies and percentages for categorical variables. Crosstab analyses were conducted to identify the association between two categorical variables using the Chi-square test or Fisher's exact test if the expected cell count does not meet the assumption. Given the small count in the suspected ventriculitis (N = 4), this category was excluded from the analysis. This was done to prevent any crossover contamination of the data by the suspected ventriculitis group into the other two groups (confirmed & no ventriculitis). These two groups are essentially positive and negative for ventriculitis, respectively, and the intermediate suspected ventriculitis group does not clearly belong in either one based on established criteria in the literature.[12 (link)] Independent t-test was adopted to analyze whether there is a statistically significant difference between patients with and without ventriculitis. All statistical tests were two-sided. P value <0.05 was considered to be statistically significant.
4
Evaluating Paramedic-EP Condition Assessment
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We conducted all statistical analyses using the SAS software for Windows version 9.3 (Cary, NC). Descriptive statistics were presented as frequencies and proportions for categorical variable. We performed a crosstab analysis to assess the inter-rater reliability (Kappa statistic) between paramedics’ and EPs’ assessment on patients’ conditions. All statistical analyses were two-sided. We considered p-value <0.05 to be statistically significant.
5
Factors Influencing COVID-19 Mental Health
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We conducted all statistical analyses using the SAS software for Windows version 9.3 (SAS Institute, Cary, NC). Descriptive statistics are presented as means and standard deviations for continuous variables, and frequencies and proportions for categorical variables. Chi-square statistics were conducted comparing whether staff were overwhelmed by the stress of COVID-19 between sociodemographic factors and scores on the PHQ-9 and GAD-7. Logistic regression analyses were conducted to examine predictors for a PHQ-9 score ≥10 and a GAD-7 score ≥8. These predictors included occupation, age, gender, years in current position, being overwhelmed by the stress of COVID-19, and being in contact with a patient either suspected to have COVID-19 or confirmed to have COVID-19. All statistical analyses were two-sided. p-value ≤ 0.05 was considered to be statistically significant.
6
Analysis of Deprivation Effects on Growth
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Statistical analyses of the data were done with the SAS software for Windows, version 9.3 (Copyright, SAS Institute Inc., Cary, NC, USA). All data were checked for normality of distribution and homogeneity of variance. The data fulfilled these assumptions and were evaluated by analysis of variance (ANOVA) using the MIXED procedure in SAS/STAT software. The model for the measured parameters contained the fixed block effect replicate (1–3), deprivation treatment (C, DF, DU, DA), age (days 7, 21 and 35), sex (male and female) and deprivation treatment × age interaction. Sow was also included as a random effect. Additionally, least square means (LS means) and their standard errors (SE) were computed for each fixed effect in the model, and all pairwise differences between LS means were tested using the Tukey-Kramer procedure. Significance was defined as p ≤ 0.05.
7
Diastolic Dysfunction Assessment in PDD
8
Ambulance Transport Time Analysis
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The primary outcome was the difference between the median of ETA and TOA. Additional variables assessed include time of day and month during which the transport took place. We analyzed data using the SAS software for Windows version 9.3 (Cary, NC). Descriptive statistics were presented as median and interquartile for continuous variable, and frequencies and proportions for categorical variables. We conducted non-parametric Wilcoxon Rank Sum-test to compare whether or not the difference of median ETA and TOA was different from zero. Kruskal-Wallis rank test was conducted to identify whether the difference of median ETA and TOA was different by month and time of the day, respectively. All statistical analyses were two-sided. p-value<0.05 was considered to be statistically significant.
9
Nonparametric Analysis of PCOS Metabolic Factors
10
Nonparametric Statistical Analysis of Variables
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Comparisons among groups, often including nonnormally distributed elements, were analyzed using the Wilcoxon-Mann-Whitney test and relations among variables were tested by Spearman's correlation coefficients. Nonnormally distributed variables were expressed as median [interquartile ranges]. The significance of P value was set at <0.05. All statistical analyses were carried out by means of SAS software for Windows, version 9.3 (SAS Institute Inc., Cary, NC, USA).
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