The results were expressed as mean ± SD of triplicate data. Experimental data were analysed using SAS system for Windows V 9.1 (SAS Institute Inc., NC, USA).
Sas system for windows v 9
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The SAS System for Windows V 9.1 is a comprehensive software suite that provides a powerful and flexible platform for data analysis, reporting, and decision-making. It offers a range of tools and capabilities for data management, statistical analysis, and business intelligence. The core function of the SAS System is to enable users to access, manipulate, analyze, and present data from a variety of sources, supporting a wide range of applications and industries.
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8 protocols using sas system for windows v 9
1
Statistical Analysis of Experimental Data
2
Concentrate Intake and Digestibility Analysis
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Liveweight gain was calculated from the difference between final and initial weights. Apparent digestibility of DM and OM, and digestibility of NDF were calculated as intake (kg DM/day) minus faecal output (kg DM/day) divided by intake (kg DM/day) expressed as a percentage. Substitution rate was calculated as the difference between control roughage (grass and rice strass) intake and treatment roughage intake, divided by concentrate intake.
Intake, LWG, and digestibility response variables were analysed in SAS (SAS Institute: The SAS system for Windows. v. 9.1. Cary, NC; 2003) [12 ] using PROC GLM with concentrate as a fixed effect, and a random block. Fisher’s protected LSD was used to test differences (P < 0.05) among means where the overall F test was significant. Regression equations were developed using the PROC GLM procedure, based on initial body weight and amount of concentrate offered and their quadratic terms as explanatory variables. Variables were dropped from the regression model if non-significant (P < 0.05) in the presence of other explanatory variables, and the regression re-calculated until only significant variables remained. The coefficient of determination (r2) and the overall F-test significance of the regression were determined. The regression equation is not presented where the overall F-test was not significant.
Intake, LWG, and digestibility response variables were analysed in SAS (SAS Institute: The SAS system for Windows. v. 9.1. Cary, NC; 2003) [12 ] using PROC GLM with concentrate as a fixed effect, and a random block. Fisher’s protected LSD was used to test differences (P < 0.05) among means where the overall F test was significant. Regression equations were developed using the PROC GLM procedure, based on initial body weight and amount of concentrate offered and their quadratic terms as explanatory variables. Variables were dropped from the regression model if non-significant (P < 0.05) in the presence of other explanatory variables, and the regression re-calculated until only significant variables remained. The coefficient of determination (r2) and the overall F-test significance of the regression were determined. The regression equation is not presented where the overall F-test was not significant.
3
Cancer Care Determinants Study
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Descriptive statistics were used to characterize study sample demographics. T-tests were used for continuous variables, Cochran-Mantel-Haenszel statistics for categorical variables, and chi-square statistics or Fisher exact tests for binary variables. Logistic regression was used to determine the association between cancer type and care received, adjusting for confounders (entered into the model at a significance level of P<0.10 and retained at a significance level of P<0.05). All patient sociodemographic characteristics, health, quality of life, and communication variables were considered as potential confounders. Results are presented as unadjusted (uOR) and adjusted odds ratios (aOR). Statistical inferences were based on two-sided tests with P<0.05 as the cutoff for statistical significance. Data analysis was conducted using SAS System for Windows v. 9.1 (SAS Institute, Inc., Cary, NC, USA).
4
Correlating Tumor Characteristics and FDG-PET
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Continuous variables are shown as the mean ± standard deviation (SD) with or without median (interquartile range), and the categorical variables are presented as the frequency and percentage. The differences between patient age and histopathological findings in ILCs and IDCs were compared using the Wilcoxon rank sum and Kruskal-Wallis tests. The association between the histopathological variables (for example, histological type, histological grade, ER and PR status, HER2, EGFR, and Ki-67 of the primary tumor) and the SUVmax were compared in each group of total carcinomas, ILCs, and IDCs using the Wilcoxon rank sum and Kruskal-Wallis tests. The correlations between the tumor size and the SUVmax were determined by the Spearman correlation coefficient and P value.
Significance was established at P < 0.05. The evaluation of the results was performed using the SAS system for Windows V 9.1 (SAS Institute, Cary, NC, USA).
Significance was established at P < 0.05. The evaluation of the results was performed using the SAS system for Windows V 9.1 (SAS Institute, Cary, NC, USA).
5
Generalizability Analysis of Spanish Championships
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In order to verify that the data were reliable (Gr) and generalisable (Ga) a variance component analysis was implemented, using the least squares and maximum likelihood estimation procedures (p < 0.01) and generalisability analysis (Gr > 0.800; Ga > 0.800). Generalisability theory (G theory) is a theory of the multifaceted errors of measurement; it assumes that any measurement situation contains infinite sources of variation. Generalisability analysis combines the concepts of reliability, validity and precision (Blanco-Villaseñor et al., 2014 ). We then developed a multiple linear regression analysis for each of the 8 years of Spanish Championships. We used the statistical packages SAS System for Windows v. 9.1. (SAS Institute Inc., Cary, NC, USA), SAGT v.1. (University of Málaga, Spain) and SPSS v19.0 for Windows (SPSS Inc., Chicago, IL, USA), respectively.
6
Comparative Survival Analysis of Treatment Outcomes
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Continuous variables are presented as median (with 25th and 75th percentile) with comparisons made using the Wilcoxon rank sum test. Categorical variables are shown as frequency and percentages with comparisons made using χ2 tests or the Fisher's exact test as appropriate.
To adjust for differences in baseline characteristics, we generated multilevel Cox proportional hazards models for each of the primary and secondary outcomes. Log‐normal frailty survival models were constructed, with the clustered hospital effect incorporated as independent and identically distributed random variables. Variable selection for each model was undertaken using a combination of stepwise selection and assessment of the Akaike information criterion (lower values indicate a better fit) before fitting the final random effects models.
Unadjusted and adjusted hazard ratios (HR) and 95% CI are reported for each outcome. Intrarater agreement was calculated using the Cohen kappa statistic.13 In all cases P<0.05 was considered significant. The analysis was carried out using the SAS system for Windows v9.3 (SAS Institute, Cary, NC).
To adjust for differences in baseline characteristics, we generated multilevel Cox proportional hazards models for each of the primary and secondary outcomes. Log‐normal frailty survival models were constructed, with the clustered hospital effect incorporated as independent and identically distributed random variables. Variable selection for each model was undertaken using a combination of stepwise selection and assessment of the Akaike information criterion (lower values indicate a better fit) before fitting the final random effects models.
Unadjusted and adjusted hazard ratios (HR) and 95% CI are reported for each outcome. Intrarater agreement was calculated using the Cohen kappa statistic.
7
Evaluating Problem-Solving Skills Training Outcomes
8
Statistical Analysis Software Comparison
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Data were analysed using IBM SPSS Statistics for Windows V.20, R for Windows V.2.13.1 and SAS System for Windows V.9.3 (SAS Institute Inc, Cary, North Carolina, USA).
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