Sas system for windows 9

Student’s t test or analysis of variance was used for continuous data, and categorical variables were compared using the Chi-squared analysis. Statistical analysis was performed with SAS System for Windows 9.4 software (SAS Institute Inc., Cary, NC). All tests were two-tailed analyses, and P <  0.05 was considered statistically significant.

Descriptive statistics were employed to characterise patients and controls.
Continuous variables with a normal distribution were compared using the
Student’s t-test for independent samples, whereas variables with a skewed
distribution were compared by Mann–Whitney U tests for independent samples.
Differences between groups were investigated the chi-square test in the case of
nominal data. When expected frequencies were low, Fisher’s exact test was used.
Odds ratios, crude and adjusted for confounders known to be associated with
either diabetes or aPL (i.e. age, gender and smoking) and corresponding 95%
confidence intervals were calculated by use of logistic regression to assess the
association between glucose variables and aPL IgG positivity in the total
cohort. Correlations were calculated using the Spearman rank correlation
coefficient. Calculations were performed using SAS software (SAS system for
Windows 9.4, SAS Institute Inc., Cary, NC, USA).
A two-sided p-value < 0.05 was considered statistically
significant.

Descriptive statistics was used to characterize the data. Changes in median values between index and follow-up were tested by signed rank test; p values <0.05 were regarded as significant. All analyses were carried out by use of the SAS statistical software (The SAS system for Windows 9.4, SAS Institute Inc., Cary, NC, USA).

Patient baseline characteristics are presented as counts and percentages. The standardized mean difference (SMD) was used to weigh the differences in baseline characteristics between patients with normal liver enzyme level (group 1) and those with elevated liver enzyme level (group 2). An SMD of < 0.1 was considered to indicate a nonsignificant difference between the two groups. We used Cox regression models to assess the hazard ratios (HRs) for postoperative mortality. The HRs were adjusted for all patient characteristics.
Propensity score matching was used to balance the patient characteristics between the two groups (Lunceford 2017 (link)). We defined a propensity score as the probability that a patient had an elevated liver enzyme level given observed covariates. A logistic regression model with baseline characteristics was used to estimate propensity scores. We employed propensity score matching as a sensitivity analysis to verify the robustness of our findings.
Statistical significance was indicated at P < 0.05. All statistical analyses were performed using the SAS System for Windows 9.4 (SAS Institute, Cary, NC, USA).