Sas university edition software

Statistical analysis was performed using SPSS software version 21.0.0.0 (IBM), GraphPad Prism version 6.0 (GraphPad Software), and SAS University Edition software (SAS Institute Inc). For multiple comparisons, the nonparametric one-way ANOVA with Dunnett post hoc test or the FREQ and COMPPROP procedures (SAS) were used for continuous variables or group analyses, respectively. For univariate disease-free survival, the log-rank and the Kaplan-Meier estimates were calculated, and the Cox proportional hazard model was applied in multivariate analyses. The Receiver Operating Characteristic (ROC) curve analysis was performed under parametric distribution assumption (Eng J. ROC analysis: web-based calculator for ROC curves. Baltimore: Johns Hopkins University. Available at: http://www.jrocfit.org/, accessed on July 12, 2016). The p value less than 0.05 was regarded as indicating statistical significance.

All results (enzymatic, coagulant and haemorrhagic) were expressed as the mean ± SDM and statistical comparisons were done using Student’s t test or ANOVA followed by the Tukey–Kramer test, with p < 0.05 indicating significance. Data were analyzed using SAS University Edition software (SAS Institute Inc., Cary, NC, United States).

Continuous variables were checked for normality distribution by Shapiro–Wilk test. Differences in continuous variables were investigated by Student t-test and Mann–Whitney U test as appropriate. Differences in categorical variable were evaluated by Fisher exact test and Chi square test as appropriate. SDSC scores were defined as pathologic score if scores were total ≥ 71, DIMS ≥ 17, SBD ≥ 7, DOA ≥ 6, SWTD ≥ 14, DES ≥ 13, and SH ≥ 7 [15 (link)]. CPP incidence was calculated as the ratio of CPP diagnosis and number of outpatients visits a year. Continuous data are expressed as mean ± standard deviations (DS). Categorical data are reported as frequency. A p value < 0.05 was considered statistically significant. All analyses were performed with SAS University Edition software.

The applications for analysis of behavioral data (ImageBT, ImageLD, ImageEP, ImageSI, ImageHC, ImageCSI, ImagePS/TS, ImageFZ, ImageBM, ImageTM, ImageYM) were based on ImageJ (https://rsb.info.nih.gov/ij) and developed by T. Miyakawa [38 (link)]. Statistical analysis was performed with the use of SAS University Edition software (SAS Institute, Cary, NC). Normality of data was first assessed with the Shapiro–Wilk test, and homogeneity of variance between genotypes was examined with the F-test for each behavioral measure. If the normality assumption was not met, the Wilcoxon rank sum test was applied for comparisons between genotypes. If data were normally distributed and variance was homogeneous between genotypes, comparisons were performed with Student’s t test. If homogeneity of variance was not assumed, Welch’s t test was applied instead of Student’s t test. ANOVA was also conducted for all tests. All statistical analysis values, including ANOVA results, are included in Additional file 1: Table S1.

Koenker and Bassett (1978) first introduced the key idea of quantile regression [7 (link), 18 , 39 –41 (link)]. This procedure has an advantage over the conventional common least-squares method. It does not accept a steady effect of the independent factors over the dependent variable’s whole distribution [36 (link), 39 ]. This methodology was utilised to consider a heterogeneous impact of every determinant alongside various percentiles of the dependent variable’s conditional distribution [37 (link)].
Koenker and Bassett (1978) show that the empirical quantile function is the solution of the minimisation problem defined by [40 (link)]:
$$\begin{array}{l}{\widehat{\beta }}_{\tau }=\underset{{\beta }_{\tau }\in R^K}{argmin}\left\{{\displaystyle \sum _{i:y_i\ge x_i^{\prime }{\beta }_r}}\tau \left|y_i-x_i^{\prime }{\beta }_{\tau }\right|+{\displaystyle \sum _{i:y_i<x_i^{\prime }{\beta }_r}}(1-\tau )\left|y_i-x_i^{\prime }{\beta }_{\tau }\right|\right\}\\ =\underset{{\beta }_{\tau }\in R^K}{argmin}{\displaystyle \sum _i}{\rho }_{\tau }\left|y_i-x_i^{\prime }{\beta }_{\tau }\right|\end{array}$$
With ρ_τ(z) can be defined as:
$${\rho }_{\tau }(z)=\{\begin{array}{l}\tau (z)\mathrm{if}z\ge 0\\ (\tau -1)z\mathrm{if}z<0\end{array}=(\tau -I(z<0))z$$
Let x_i where i = 1, …, n a sample, a K×1 vector of regressors, $y_i=x_i^{\prime }{\beta }_{\tau }+{\epsilon }_{{\tau }_i}$ , 0<τ<1, ρ_τ(z) is the check function, and I(•) the usual indicator function.
In this study, quantile regression analysis was performed to identify the independent variables related to child anthropometric Z score over the seven (5^th, 10^th, 25^th, 50^th, 75^th, 90^th, and 95th) percentiles. This analysis was performed using the SAS University Edition software and SAS® OnDemand for Academics.

Independent chi-square tests were used to examine the initial basic characteristics of the groups, such as gender, age, residence, living conditions, education, BMI, alcohol consumption, smoking status, exercise, staple food intake, main flavor, vitamins intake, self-rated health, and history of chronic diseases.
We constructed multilevel logistic regression models to account for potential confounders. They were mainly analyzed by five models: Model 1 was a multiple logistic regression model of dietary factors and functional impairment without any adjustment. Model 2 was adjusted according to sociodemographic information. Model 3 was adjusted according to sociodemographic information and physical health and living habits. Model 4 was adjusted according to sociodemographic information, physical health and living habits, and other dietary factors. Model 5 was adjusted according to sociodemographic information, physical health and living habits, other dietary factors, self-rated health, and history of chronic diseases.
Data preprocessing, database establishment, and statistical analysis were all completed by SAS University edition software (Copyright © 2012–2020, SAS Institute Inc., Cary, NC, USA) unless otherwise indicated. p < 0.05 was used as the statistical index of significance test.