2.4 Statistical Analysis
Continuous data are expressed as mean ± SD, and categorical data are expressed as numbers and percentages. We examined the relationship between QTc interval and patient characteristics and RA disease activity using Student’s t-test for normally distributed data or Wilcoxon rank-sum test for non-normally distributed data. In addition, we assessed the association between QTc interval and RA medication using the Wilcoxon rank-sum test to investigate the effects of medications on the QTc interval.
Univariate associations between the QTc interval and clinical variables were assessed using Pearson or Spearman correlations based on the distribution of variables. Multigroup comparisons were performed using the analysis of variance, and further between-group comparisons were performed using Tukey’s test.
Variables significantly associated with the QTc interval were used as potential independent variables in a multivariate linear regression model. We also included each patient’s age and sex because these parameters were reported to be associated with the QT interval11,12. We developed multivariate linear regression models using these potential and predetermined covariates. In this study, statistical significance was determined by p <0.05. Data were analyzed using JMP 15.0 (Statistical Analysis System, Institute Inc., NC, USA).