Figure 2. Propensity score matching plot.
Continuous variables were summarized using descriptive statistics such as mean and standard error. All categorical variables were summarized using frequencies and percentages. To further investigate the balance of prognostic variables between the case and control groups, univariate analyses were performed using the t-test or chi-square test upon specific requirements. All the variables associated with prognostic factors and disease history demonstrated no significant impact on the outcome, except one variable, LVEF (see Table I). The conditional logistic regressions (an extension of logistic regression which considers stratification and matching) were used to examine the potential association between the exposure(s) and the outcome while adjusting for the covariate LVEF. A unique matching ID for each pair was generated to indicate the stratum. Thus, the matched variables were controlled simultaneously because their combined information was already incorporated into the matched pairs by this ID. To identify the best-fitting model, we conducted a model selection process based on 6 candidate models whose exposure variables varied (see Table II). The goodness-of-fit measurement Akaike information criterion (AIC) was used to compare these candidates. The preferred model is the one with the minimum AIC value. Thus, Model 1 was the final model for our statistical results and inference. Odds ratios and 95% confidence intervals were reported in Table III and displayed in the forest plot (Figure 3). All analyses were performed at a two-sided 5% Type I error rate by SAS 9.4 (SAS Institute, Cary, NC).