After data collection, a database was set up using EpiData 3.1 (EpiData Association, Odense, Denmark). First, the level of psychological distress in accordance with the K10 definition was presented by sociodemographic characteristics, clinical conditions and experienced stigma. Second, univariate analysis was used to test the associations between the variables and psychological distress by adopting Pearson’s Chi-Square. Third, variables with p value less than 0.05 from the univariate analysis were further analyzed in the multiple logistic regression models. Odds ratios (ORs) and 95% confidence intervals (CIs) were used to present the strengths of the associations. Finally, the predictive capability of the logistic model was assessed adopting the Receiver Operating Characteristic (ROC) curve and the area under the ROC curve (AUC). The statistical significance p value was set at p < 0.05. Statistical Analysis System (SAS 9.2 version) software (SAS Institute Inc., Cary, NC, USA) was used in data analysis.
Sas 9.2 version
Manufactured by SAS Institute
Sourced in United States
SAS 9.2 is a software solution for data management, analysis, and reporting. It provides a comprehensive set of tools for data integration, statistical analysis, and business intelligence. The software is designed to handle large and complex datasets, enabling users to extract valuable insights and make informed decisions. SAS 9.2 is widely used in various industries, including finance, healthcare, and academia.
Automatically generated - may contain errors
13 protocols using sas 9.2 version
1
Psychological Distress Predictors Analysis
2
Genotype-Environment Interaction Analysis
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Collected quality data were subjected to combined analysis of variance to assess variation among genotypes and quantify the significance of genotype-by-environment interaction using PROC GLM of SAS 9.2 version software (SAS Institute, 2008 ). Additionally, to determine the magnitude of GEI and assess genotypes adaptation, additive main effect and multiplicative interaction (AMMI) was applied using GenStat ver.18 statistical package (Payne, 2015 ) following Gauch (2013) model: Where, Yij is the yield of the ith genotype in the jth environment, gi is the mean of the ith genotype minus the grand mean, ej is the mean of the jth environment minus the grand mean, n is the number of components retained in the model, λk is the square root of the eigenvalue of the PCA axis k, αik and γjk are the principal component scores for PCA axis k of the ith genotype and the jth environment, respectively, and eij is the residual.
3
Telomere Length in Chronic Lymphocytic Leukemia
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Nonparametric analysis of covariance adjusting for age was performed to compare the TL between CLL and normal control groups. Correlation coefficients for the CBC profile, BM lymphocytes, Richter syndrome, ZAP70, cytogenetics and gene mutations, TL, and the percentage of cells with the shortest TL (STL%) were calculated by Kendall’s tau-b (Tb) correlation test. TLs between subgroups were compared using the Mann-Whitney test and Kruskal-Wallis rank sum test. Survival was estimated using the Kaplan-Meier (K-M) method, and differences between the survival curves and hazard ratios were analyzed using the Cox proportional hazards model and Harrell’s C-index. Factors with p values less than 0.2 in the univariate analysis were entered into the multivariate analysis. Overall survival (OS) was calculated from the date of diagnosis until the date of the last follow-up or death. Time-to-First-Treatment (TTT) analysis was calculated from the interval of time between the diagnosis and the date of first CLL treatment [16 (link)]. Statistical analyses were performed using R software (http://www.r-project.org ), IBM SPSS Statistics Version 23.0. (Armonk, IBM Corp.), SAS 9.2 Version (SAS Institute Inc., Cary, NC, USA.), and STATA 15.0 (StataCorp. 2017. Stata Statistical Software: Release 15. College Station, TX: StataCorp LLC). A p value < 0.05 was considered statistically significant.
4
Comparing Pain Relief Outcomes
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The data will be collected and analyzed according to the intention-to-treat principle. Standard statistical techniques will be used to describe patients’ characteristics in both groups. We will compare baseline characteristics in both groups and, if incomparability appears, we will perform the secondary analysis, adjusting for differences. The primary outcome, VAS, will be compared between both groups using analysis of variance for repeated measures. If adjustment for possible baseline incomparability is needed, analysis of covariance will be done.
All data were analyzed statistically using SAS 9.2 version statistical software. Measurement data were expressed as mean ± standard deviation , and analyzed using a t test; rates were compared using the chi-square test. P < 0.05 will be used to indicated that the difference was statistically significant.
All data were analyzed statistically using SAS 9.2 version statistical software. Measurement data were expressed as mean ± standard deviation , and analyzed using a t test; rates were compared using the chi-square test. P < 0.05 will be used to indicated that the difference was statistically significant.
5
Herpes Zoster Risk in Diabetes
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The distributions of sex, age, and comorbidities between the diabetes mellitus group and the nondiabetes mellitus group were analyzed by using the Chi-square test for categorical variables and the t test for continuous variables. The incidence of herpes zoster was estimated as the event number of herpes zoster found during the follow-up period, divided by the total follow-up person-years for each group. The incidence rate ratio with 95% confidence interval (CI) of herpes zoster for diabetes mellitus group versus non-diabetes mellitus group was estimated by using Poisson regression, stratified by sex and age. All variables were included in a univariable model. Those variables found to be statistically significant in a univariable model were further included in a multivariable model. The multivariable Cox proportional hazards regression model was used to calculate the hazard ratio (HR) and 95% CI of herpes zoster associated with diabetes mellitus and comorbidities. All statistical analyses were performed by using the SAS 9.2 version (SAS Institute, Cary, NC). Two-tailed P < .05 was considered statistically significant.
6
Patient Factors Influence Quality of Life
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To study the influence of patient-related factors on the values of the overall QOL score and domain scores, we used the Multiple Linear Regression (SAS, 9.2 version, 2002–2008, Cary, NC, USA) model, with stepwise criteria of variable selection. The variables were transformed into ranks due to the lack of normal distribution. P < 0.05 was considered significant.
7
Factors Associated with WMH Progression
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A descriptive analysis was done on the pattern of WMH change over time, and possible factors associated with each changing pattern were analyzed. To compare baseline characteristics between patients with progression and those without, we used Student's t test for age and time interval, Mann Whitney U test for hsCRP, Chi-square test for sex, hypertension, diabetes, hyperlipidemia, and smoking, and Fisher exact test for TOAST subtype. Regarding regression, Student's t test (age, time interval), Mann Whitney U test (hsCRP), the Chi-square test (sex, hyperlipidemia), and Fisher exact test (hypertension, smoking, TOAST subtype) were used. In the multiple logistic regression model, factors with P<0.3 according to univariate analysis were entered. SAS 9.2 version was used. This study was approved by institutional review board of our hospital. An informed consent was not obtained because this study is retrospectively performed.
8
Analyzing Endothelial RAGE Levels
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The two-sided Student t test was used to analyze the proportion of change in the levels of endothelial RAGE. Differences were considered statistically significant at p<0.05. The statistical program used was SAS 9.2 version (SAS institute, Inc., Cary, NC).
9
Weight Control Education Comparison
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All statistical analysis was conducted utilizing SAS 9.2 version (statistical analysis system, SAS Institute, Cary, NC, USA). Data were described with means and standard deviations. Comparisons between low-carbohydrate and low simple-sugar diet education group and general weight control education group were analyzed with student t-test. Significance level of all statistical verification was 0.05.
10
Randomized Field Experiments Analysis
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Nethouse and field experiments were carried out in complete RBD in 10 and 5 replications, respectively. Data were subjected to analysis of variance (ANOVA) and least significant difference (LSD) at p ≤ 0.05 using SAS 9.2 version. Data were compared with Duncan’s multiple range test at p ≤ 0.05. Graphs were prepared using statistical software Origin 9.0.
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