Sas statistical software for windows version 9

Mean and standard deviation were used to describe all continuous variables when the data were uniformly distributed. For continuous variables that were not uniformly distributed, median and interquartile range statistics were used. Analysis of variance (ANOVA) was used to compare the means of continuous uniformly distributed variables. Chi-squared tests were used to analyze the categorical variables, which are presented as numbers and percentages. Assessment of differences in overall survival between the periods studied was conducted with Kaplan–Meier statistics and log-rank tests. SAS statistical software for Windows version 9.4 was used for all statistical analyses. A p-value of <0.05 was used to define statistical significance.

SAS statistical software for Windows version 9.2 (SAS Institute Inc., Cary, NC, USA) was used for both data management and statistical analysis. The data presented in this paper are primarily of a descriptive character, with some statistical tests between categorical variables using chi-square testing.

Descriptive statistics were performed; results were reported as frequencies with percentages, means with standard deviations, or medians with ranges. Univariate parametric analyses utilized Fisher’s exact and chi-square analyses for categorical data and students T-test for continuous variables. Non-parametric tests were performed where appropriate. Paired univariable analyses were conducted to determine treatment responses. Repeated measures linear regression analysis was performed for comparative and predictive modeling of hearing thresholds to account for paired ears within patients. All tests considered p <0.05 as statistically significant. Statistical analyses were performed using SAS statistical software for Windows Version 9.3 (SAS Institute, Cary, North Carolina, USA).

The incidences of newly diagnosed breast cancer, ovarian cancer, or uterine cancer in PID patients and controls were calculated, and independent t tests and chi-squared tests were used to examine differences in demographic characteristics between the PID patients and controls. A Cox proportional-hazards regression model was constructed to calculate the hazard ratio (HR) of breast cancer, ovarian cancer, or uterine cancer of the PID and control cohorts, respectively. Control variables, such as age, urbanization, monthly income, common co-morbidities including hypertension, diabetes mellitus, dyslipidemia, congestive heart failure, chronic pulmonary diseases, coronary artery diseases, and cerebrovascular diseases were included as covariates in the multivariate model to calculate adjusted HR. SAS statistical software for Windows, version 9.3 (SAS Institute, Cary, NC, USA), was used for data extraction, computation, data linkage, processing, and sampling. All other statistical analyses were performed using SPSS statistical software for Windows, version 20 (IBM, Armonk, NY, USA). Results for comparisons with a P value of less than .05 were considered as a statistically significant relationship.