Sas system for windows version

We used SAS software (SAS System for Windows, version 9.2; SAS Institute, Cary, NC, USA) to perform the statistical analysis for this study. The PET/CT and no PET/CT groups were compared using Wilcoxon rank-sum tests for continuous variables and chi-squared or Fisher’s exact tests for categorical variables. In order to reduce the bias, we used propensity score matching of age, sex, smoking status, cell type, operative method, clinical stage, and pathological stage. There were 2649 patients in both groups after the propensity match.
Survival curves were plotted using the Kaplan–Meier method, and the difference in survival was calculated by the log-rank test. The survival rates of pathological stages I, II, and III and the 1-, 3-, and 5-year survival rates of all clinical stage I lung cancer patients were analyzed.
Univariate and multivariate analyses were performed with the Cox proportional hazards model. Covariates were selected based on clinical judgment. The following factors were included in the analyses: age, sex, smoking status, cell type, operative method, clinical stage, pathological stage, and PET/CT performance. Statistical analysis with a p-value less than 0.05 was considered statistically significant.

Characteristics of the study population stratified by hemoglobin levels concentration were expressed as mean ±standard deviation. Simple and partial correlation analysis adjusted for known cardiovascular risk factors and WBC were performed to evaluate reticulocytes and other existing parameters. We also performed simple and multiple linear regression analysis to evaluate the same. Since intercorrelation with systolic blood pressure was r= 0.73 (P<0.001), diastolic blood pressure was not analyzed as a confounding factor. Because TG, γ-GTP, and serum creatinine had a skewed distribution, logarithmic transformation was performed for the simple and partial correlation analysis, and linear regression analysis. All statistical analyses were performed with the SAS system for Windows (version 9.4; SAS Inc., Cary, NC). Probability values of less than 0.05 were considered to be statistically significant.

All statistical analyses were completed using SAS (SAS System for Windows, version 9.2; SAS Institute Inc., Cary, NC) or IBM SPSS (SPSS version 22; Chicago, IL). Continuous data were presented as mean ± standard deviation (SD) or [95% confidence intervals (CI)]. Categorical data are summarized as frequencies and percentages. Generalized estimating equations were used to adjust for nonindependence within endoscopist clusters for procedural level data. The differences between continuous variables were assessed using Student t-tests. The chi-squared test was used to assess differences in distributions of categorical variables. Results were considered statistically significant for a (2-tailed) p value of <0.05.

SAS software (SAS System for Windows, version 9.2; SAS Institute, Cary, North Carolina) was used to perform the statistical analysis. Overall survival was calculated by the Kaplan-Meier method, and the difference in survival was determined by the log-rank test. Overall survival was calculated based on the time period from the date of surgery to death or December 31, 2012. The date and cause of death were obtained from Taiwanese death certificates, which were updated on December 31, 2012. Continuous data were compared using the 2-tailed t test, and comparisons of categorical data were made using the χ² or Fisher exact test. Statistical analysis was considered to be significant with a p-value < 0.05.
Univariate and multivariate analyses were performed using the Cox proportional hazards model with SAS software. To investigate the factors influencing overall survival, all of the following clinicopathological factors were included in the multivariate analyses: age, sex, pathological T and N stage, pathological stage, surgical method (pneumonectomy, bilobectomy, lobectomy, wedge resection), cell type, tumor grade, and interval between surgery and chemotherapy.