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).