Data were analyzed using SAS University Edition version 9.4 (SAS Institute, Inc.). We used descriptive statistics to summarize children’s sugar intake. Linear regression models using generalized estimating equations were fitted to estimate associations between sugar intake and anthropometric measures. We used generalized estimating equations to account for any dependence between sibling participants23 and to attain 95% confidence intervals (CIs) for categories of sugar sources. The variables age and sex were identified as potential confounders and controlled for in the models that explored associations between sugar intake and children’s body weight, fat mass and waist circumference.
Sas university edition version 9
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SAS University Edition version 9.4 is a free, personal-use software package that provides access to the SAS programming language and analytical capabilities. It allows users to learn and explore the SAS software suite without requiring a commercial license.
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8 protocols using sas university edition version 9
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Examining Children's Sugar Intake Impacts
2
Multifocal HCC Surgical Outcomes
3
Snack and Beverage Contribution to Free Sugar Intake
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Statistical analyses were completed using SAS® University Edition version 9.4 (SAS Institute, Inc., Cary, NC). The percent contributions (mean ± SD) of snacks, beverages, and their FS to TE and total FS energy were estimated. These data were used to determine the proportion of children whose FS intakes exceeded < 5% TE and < 10% TE from snacks or beverage alone.
To identify snack and beverage sources of FS, the proportion of children consuming each snack and beverage category (See Additional file2 ) and each category’s FS contribution to TE were determined. The frequency and percent of children consuming each snack and beverage category (regardless of serving size) over 24-h were reported. For each participant, the energy consumed from FS per snack and beverage category relative to TE (%TE from FS) was determined by summing FS energy for the snack or beverage category, dividing by the participant’s TE and then multiplying by 100. Next, participants’ %TE from FS (mean, 95% CI) was calculated for snack and beverage categories consumed by at least 30 participants (to ensure adequate data for a meaningful summary). Generalized estimating equations were used to account for any dependence between sibling participants [23 (link)].
To identify snack and beverage sources of FS, the proportion of children consuming each snack and beverage category (See Additional file
4
Arginine Levels and Immune Challenge
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Data were first tested for homogeneity of variances and normality of studentized residuals. Performance results from 1 to 12 d of age were subjected to one-way ANOVA, and in case of significant differences, the treatments were compared by Tukey's test. All other data were subjected to 2-way ANOVA, obtaining results for each factor (Arg dietary levels and challenge) as well as their interaction. In case of significant differences, the treatments were compared by Tukey's test. Moreover, the analysis was extended to include polynomial contrasts to test linear and quadratic components of Arg levels and interaction components, such as linear and quadratic effects of Arg at each level of challenge (challenged and unchallenged). All statistical procedures were performed using SAS University Edition (version 9.4, SAS Institute, Cary, NC) following the methodology and codes described by Shim et al. (2014) and Billard et al. (2014) . Statements of significance were based on P < 0.05.
5
HAIP Insertion: Survival Outcomes
6
Evaluating Respiratory Outcomes in Obesity
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Depending on data distribution (via the Shapiro Wilks test), continuous variables are described as means with standard deviations (normally distributed) or as a median interquartile range (abnormally distributed). We used frequencies and simple proportions to summarize categorical variables. We used ANOVA to compare groups with normal BMI patients, patients with overweight, and patients with obesity. We conducted univariate, age- and sex-adjusted, and multivariate logistic regressions to evaluate the need for IMV in normal, overweight, and patients with obesity. We also adjusted for possible confounders, such as C-reactive protein levels, oxygen-saturation levels, and mean arterial pressure on admission. We conducted a linear regression analysis using continuous BMI to assess the days of a hospital stay. The same covariates were used for the logistic regression analysis.
Few patients had a normal BMI, so we conducted a sensitivity analysis defining the reference group as patients with a normal or overweight BMI. We also conducted a sensitivity analysis, defining obesity as a BMI over 35 kg/m2. All statistical tests were 2-sided, and a p-value < 0.05 was considered statistically significant. We used SAS University Edition version 9.4 statistical software (SAS Institute, Cary, NC).
Few patients had a normal BMI, so we conducted a sensitivity analysis defining the reference group as patients with a normal or overweight BMI. We also conducted a sensitivity analysis, defining obesity as a BMI over 35 kg/m2. All statistical tests were 2-sided, and a p-value < 0.05 was considered statistically significant. We used SAS University Edition version 9.4 statistical software (SAS Institute, Cary, NC).
7
Peri-operative Red Blood Cell Transfusion Impact on Hepatocellular Carcinoma Recurrence
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In addition to the primary analysis, we further conducted two subgroup analyses to examine the effect of pRBC transfusion in the perioperative period. Given the reports which document an effect in patients with microvascular invasion, We therefore investigated the effect of patients without pRBC transfusion to patients that received 3 or more pRBC units. We used a multivariable Fine and Gray competing risk regression analysis to examine risk of HCC recurrence and a multivariable Cox proportional hazard regression analysis, to examine hazard of death. The same covariates used in the propensity score matching were entered in these two models. Statistical analysis was performed with SAS University Edition version 9.4 (SAS Institute, Cary, North Carolina) and p-values <0.05 were considered statistically significant. This study conformed to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline for reporting observational research (Supplementary Table 1).
8
Analyzing Animal Performance Data
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The response variables were analyzed using the GLIMMIX procedure of SAS University Edition, Version 9.4. The data were analyzed following a completely randomized design. For all performance variables, measurements at d 0 were tested as covariates and removed from the models as they were all non-signi cant (P>0.05). For all response variables period was included as repeated measurements. Fifteen variance-covariance structures were tested for each response variable. Thus, we used the variance-covariance structure that provided the best t based on lower AIC. Least square means were considered different when P ≤ 0.05.
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