The overall response rate (ORR) was expressed with 95% confidence interval (CI), and differences between groups were statistically evaluated by Fisher's exact test. Progression‐free survival (PFS) from the initiation of each therapy was estimated using the Kaplan–Meier method, with differences analyzed by the log‐rank test. Responders to salvage chemotherapy were defined as those with a complete response (CR) or partial response (PR), while non‐responders were those with stable disease (SD) or progressive disease (PD). The differences in immunohistochemical findings between the responders and non‐responders were assessed using the Mann–Whitney U test. Data were analyzed using the SAS software program (SAS Institute Inc., Cary, NC, USA), and a two‐tailed P value < 0.05 was considered significant.
Sas software program
Manufactured by SAS Institute
Sourced in United States
SAS software program is a comprehensive analytical software suite that provides advanced statistical analysis, data management, and reporting capabilities. It enables users to access, manipulate, and analyze large and complex data sets from various sources. The software's core function is to facilitate data-driven decision-making across a wide range of industries and applications.
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23 protocols using sas software program
1
Evaluating Salvage Chemotherapy for Cancer
2
Diagnostic Performance of sIgE for Food Allergy
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Categorical data are expressed in frequencies and proportions, while continuous data are expressed in means and standard deviations (SD). Receiver operator characteristic (ROC) analysis was used to determine the area under the curve (AUC). The highest value of the Youden index was used to determine the optimal cutoff point. Sensitivity, specificity, positive predictive values (PPV), negative predictive values (NPV), and positive and negative likelihood ratios (LR+ and LR−) were calculated. We used logistic regression analysis to predict the relationships between OFC outcomes and sIgE levels measured by both ImmunoCAP and 3gAllergy. The relationships were graphically represented by probability curves. Pearson's correlation coefficient was used to measure the degree of linear correlation of the log-transformed serum sIgE values and predicted probabilities measured by ImmunoCAP and 3gAllergy. We compared OFC-positive and OFC-negative groups using the Mann-Whitney U test; P < 0.05 was considered statistically significant. All statistical analyses were planned a priori and performed using the SAS software program, version 9.4 (SAS Institute, Cary, NC, USA).
3
Comparison of Viral and Non-Viral Pneumonia
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The demographic characteristics and laboratory measurements at the time of presentation were compared between the viral pneumonia and non-viral pneumonia groups using Student's t-test for continuous variables and Fisher's exact test for categorical variables. The interobserver reliability of the radiological findings was evaluated using the kappa coefficient (κ) and was defined as poor (κ<0.00), slight (0.00-0.20), fair (0.21-0.40), moderate (0.41-0.60), substantial (0.61-0.80), and almost perfect (0.81-1.00). Two-tailed p values of <0.05 were considered to indicate statistical significance. All statistical analyses were performed using the SAS software program (version 9.4, SAS Institute, Cary, USA).
4
Fatty Liver Index and Cardiovascular Outcomes
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Continuous variables are expressed as the means ± standard deviations. Categorical data are expressed as numbers with percentages. Group comparisons were performed using a one-way analysis of variance for continuous variables and chi-square testing for categorical variables. Multivariable Cox proportional hazards regression models were used to evaluate hazard ratios (HRs) and 95% confidence intervals (95% CIs) for HF outcomes and mortality during follow up, including CV mortality. The covariates for adjustment were (1) Model 1, crude; (2) Model 2, age, sex, and body weight; (3) Model 3, covariates in Model 2 + alcohol consumption, smoking, regular exercise, and income status; (4) Model 4, covariates in Model 3 + hypertension, diabetes, dyslipidemia, and eGFR. Since the FLI can be changed during subsequent periods after the first two years, subjects whose FLI categories were subsequently changed were censored for sensitivity analysis. The p-values for interaction were evaluated through an analysis stratified by age (< 60 years vs. ≥ 60 years) [31 (link)] and BMI (< 25 kg/m2 vs. ≥ 25 kg/m2) [20 (link), 32 (link)]. A p-value less than 0.05 was considered statistically significant. Statistical analyses were performed using the SAS software program (version 9.4; SAS Institute, Cary, NC, USA).
5
Evaluating Novel Cardiac Algorithm for MACE Prediction
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Continuous variables will be presented as the mean (standard deviation) or median (interquartile range [IQR]), and categorical variables will be presented as numbers and percentages. The 30-day incidence of MACEs will be analyzed using generalized linear mixed models after adjusting for background information and seasonal effects. The clustering effect by hospital will be considered in this analysis as a random effect. The binomial distribution and identity link will be used to directly estimate the absolute differences in MACE incidence between 0/1-h algorithm care and usual care patients. The main model will include conditions (control or intervention) and steps (time periods) as categorical variables and hospitals as clusters. Differences in MACEs with a 1-sided 95% CI will be estimated in order to evaluate non-inferiority. The non-inferiority margin is preset at 1.5%. The sensitivity and negative predictive values (NPVs) for MACEs in the rule-out group and the specificity and positive predictive values (PPVs) for MACEs in the rule-in group will be calculated. Multiple imputations will be used to handle missing data. The statistical analyses will be performed using the SAS software program, version 9.4 (SAS Institute, Cary, NC, USA), and R version 4.0.3 (R Foundation for Statistical Computing, Vienna, Austria).
6
Organ Fungal Load Quantification
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The viable CFU counts were converted
to logarithms as log10(x + 1), where x = total organ CFU count, which were then evaluated by
a one-way analysis of variance (ANOVA) or two-way ANOVA, followed
by a multiple comparison analysis of variance by a one-way Tukey’s
test or Dunnett’s test (SAS Software program, Research Triangle
Park, NC). Differences were considered significant at the 95% level
of confidence. A two-way ANOVA on response type was performed separately
for each drug treatment. The difference between means of the responding
and less-responsive mouse subpopulations was determined to be highly
significant (P < 0.001).
to logarithms as log10(x + 1), where x = total organ CFU count, which were then evaluated by
a one-way analysis of variance (ANOVA) or two-way ANOVA, followed
by a multiple comparison analysis of variance by a one-way Tukey’s
test or Dunnett’s test (SAS Software program, Research Triangle
Park, NC). Differences were considered significant at the 95% level
of confidence. A two-way ANOVA on response type was performed separately
for each drug treatment. The difference between means of the responding
and less-responsive mouse subpopulations was determined to be highly
significant (P < 0.001).
7
Statistical Analysis of Infestation Rates
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Datasets were analyzed using the SAS software program (SAS Institute, 2003). Colonization and infestation rates were analyzed after arcsine transformation. Treatments with an average percentage of 0 or 100 were not included in the statistical analysis. When the overall analysis of variance (ANOVA) F statistic for the treatments was significant (p < 0.05), they were compared by Tukey test (p < 0.05). The same procedure was applied for gallery length data. The effect of treatment and rootstock on signs of boring and gumming was analyzed by nominal logistic regression. Rate of G. mellonella larvae with mycosis 7 days after inoculation were arcsine-square-root transformed and subjected to ANOVA. If differences among treatment means were found to be significant (p < 0.05), Tukey’s test (p < 0.05) was used for multiple comparisons among means for the treatments and time post-inoculation. When one or two treatments showed zero variation, 95% confidence limits were calculated for the means of the remaining treatments. Two treatments with variations were compared by Student’s t-test. A significant difference was declared from 0 or from 100 if the transformed value of 0 or 100, respectively, was not included in the confidence interval. For the t-test and confidence limits, the significance and confidence levels were set at 0.05/3 = 0.017 in accordance with the Bonferroni correction.
8
Metabolic Syndrome Prevalence and Sleep
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Because of skewed distributions, natural logarithmic transformation was performed for triglycerides. Mean values and upper and lower 95% confidence limits were exponentiated and presented as the geometric mean±standard deviation (SD), where the SD was approximated as the difference in the exponentiated confidence limits divided by 3.92, the SD value in a 95% confidence interval for normally distributed data. Smoking and alcohol histories were used as dummy variables and were divided into habitual use or not. An analysis of variance (ANOVA) and post-hoc analyses with Fisher's exact test were used for continuous variables. A chi-square test and residual analysis were used for categorical variables. In addition, Mann-Whitney U tests were performed. The prevalence of MetS was divided into its existence or not. Odds ratios of the prevalence of MetS were evaluated by sex (men and women), age (＜60, 60-69, 70-79, and 80-89 years old), and current alcohol and smoking habits (yes and no). Sleep duration was divided into the following 5 categories: ＜6, 6-7, 7-8, 8-9, and ≥9 hours per day. Univariate and multivariate logistic regression analyses adjusted for the age, sex, BMI, drinking and smoking habit were performed to evaluate odds ratios within each sleep duration group. All statistical analyses were performed using the SAS software program, version 9.4 (SAS, Cary, USA).
9
Liver Disease Biochemical Analysis
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Descriptive statistics were used to summarize the patient characteristics. The concentrations for the biochemical data and the BAs were log-transformed to approximately normalize the distributions. The least square geometric mean of the biochemical data and the BAs were estimated for the different etiologies of liver diseases and were compared between them using multiple linear regression models adjusted for sex, age, body mass index (BMI), alcohol consumption, type of liver disease, dyslipidemia, diabetes mellitus, and the use of UDCA. A significance level of 0.05 was used for all statistical tests, and two-tailed tests were applied. All statistical analyses were performed using the SAS software program, version 9.2 (SAS Institute, Cary, NC).
10
Statistical Analysis of Genetic Factors in Cancer Detection
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Values were expressed as mean and standard deviation (SD) or in percentage. P values for categorical data were calculated using the Chi‐square test for homogeneity or the Cochran‐Mantel‐Haenszel test for trend, where appropriate. The general linear model was used to test for trends of mean values across groups. Detection rates of cancer were compared between study periods adjusted for age using the Cochran‐Mantel‐Haenszel test. The multivariate odds ratios (ORs) and the 95% confidence intervals (CIs) were calculated using multiple logistic models. We combined the ADH1B*1/*2 genotype carriers and the ADH1B*2/*2 genotype carriers into a group because of the semidominant nature of the ADH1B*2 allele.19 , 20 Statistical significance was defined as a P value <.05. All the statistical analyses were performed using the SAS software program (version 9.4; SAS Institute).
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