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Sas software version 9.2 for windows

Manufactured by SAS Institute
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SAS® software version 9.2 for Windows® is a comprehensive software suite that provides advanced analytics, reporting, and data management capabilities. It supports a wide range of data sources and offers a powerful programming language for statistical analysis, predictive modeling, and data visualization.

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4 protocols using sas software version 9.2 for windows

1

Low-Tie Ligation: Anastomotic Leakage Assessment

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It was hypothesized that low‐tie ligation would decrease the rate of anastomotic leakage from 15 to 6 per cent. Using a power of 80 per cent and α of 0·05, a sample size of 362 patients was needed. A dropout rate of approximately 10 per cent was anticipated. Therefore, 400 patients had to be included in this study. Enrolment was scheduled for 5 years after inclusion of the first patient.
Data were analysed according to the intention‐to‐treat principle. SAS® software version 9.2 for Windows® (SAS Institute, Cary, North Carolina, USA) was used for statistical analysis.
Categorical variables are presented as frequencies and percentages. Continuous variables are presented as mean(s.d.) values. The χ2 test and Student's t test were used to compare categorical and continuous variables respectively. Survival was analysed by the Kaplan–Meier method, and the difference between high‐ and low‐tie ligation was analysed with the log rank test. Risk factors for anastomotic leakage were assessed by logistic regression using a forward method. Variables with P < 0·100 were entered into multivariable analysis. P < 0·050 was considered statistically significant. All analyses were two‐sided.
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2

Alcohol and Substance Use Prevalence

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The proportion of patients with problem alcohol or substance use documented in the previous two years was estimated with a 95% confidence interval for the proportion, accounting for the structure of the dataset with patients clustered within practices. Demographic and healthcare utilisation variables were summarised using graphical and numeric descriptive statistics. For those patients with problem alcohol and/or substance use documented, information on treatment was summarised using graphical and numeric descriptive statistics. The association between categorical variables was tested using chi-square tests and median consultations rates were compared across groups using non-parametric tests. A 5% level of significance was used for all statistical tests. SPSS Statistics Version 21 for Windows and SAS software Version 9.2 for Windows (SAS Institute, Inc.) were used to carry out the analysis with the SAS procedure SURVEYFREQ used to account for clustering.
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3

Genetic Factors Influencing BoHV-1 Infection

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To establish the association between alleles, genotypes, susceptibility, neutrality, or resistance to infection with BoHV-1, odds ratios were calculated using a logistic regression model. If the OR is greater than 1, the relationship of allele or genotype is resistant and there is low risk of being positive for BoHV-1, but if it is less than 1, the relationship is susceptible and there is high risk of being positive for BoHV-1; neutrality is considered when the OR is equal to 1 or when it is not significant. Fisher’s exact test was used to determine the significance of OR (p < 0.05) and, as an additional test, Chi-square (χ2) Mantel–Haenszel test to determine general association between alleles and infection with BoHV-1 was conducted. OR calculations were executed in SAS® software version 9.2 for Windows (SAS Institute Inc, Cary NC) and significance calculations were executed in StatCalc 3.0 (Professor K. Krishnamoorthy, Dept of Mathematics, UL Lafayette, LA, software Lafayette, LA).
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4

Survival Analysis of Transplant Outcomes

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The median, minimum, and maximum of each continuous variable and the counts and percentages of each categorical variable by transplant status were tabulated. The Wilcoxon rank-sum test was used to assess the association between transplant status and continuous variables, with p-values computed using the normal approximation. Fisher’s exact test and its generalizations were used to assess the association between transplant status and categorical variables. Kaplan-Meier survival curves and univariate Cox proportional hazards regression analysis were used to estimate survival and the effects of clinical and demographic variables on survival. The cumulative incidence of graft-versus-host disease (GVHD) was estimated using the method of Fine and Gray [10 ] accounting for the competing risk of death. All statistical analyses were performed using SAS software version 9.2 for Windows (SAS Institute, Inc., Cary, NC) and R version 2.13.0 (The R Foundation for Statistical Computing). P values < 0.05 were considered statistically significant.
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