2.2. Statistical Analysis

Continuous variables (age) were expressed as mean and standard deviation (SD), and tested for differences with independent-sample t test. Categorical variables (sex, ethnicity, comorbidities, and pharmacological therapy) were expressed as absolute frequencies and percentages, and tested for differences with chi-squared test. The TriNetX platform was used to run 1:1 propensity score matching (PSM) using logistic regression. The platform uses ‘greedy nearest-neighbour matching’ with a caliper of 0.1 pooled standard deviations and difference between propensity scores ≤0.1. Covariate balance between groups was assessed using standardised mean differences (SMDs). Any baseline characteristic with a SMD between cohorts <0.1 is considered well-matched [9].

HR and 95% CI were calculated following PSM, and displayed as Kaplan-Meier survival curves with log-rank tests. No imputations were made for missing data. Two-sided p-values < 0.05 were accepted as statistically significant. Statistical analysis was performed using the TriNetX Analytics function in the online research platform.