2.4. Statistical analysis
Baseline and early mortality data were pooled using meta-analysis of means or proportions, with individual study effect size accounted for using inverse variance methods. Results were displayed as raw values as well as percentages with 95% confidence intervals (CI) or 95% credible intervals. Graft patency and long-term mortality for arterial, venous or mixed CABG were analyzed across all arm-level studies, with direct and indirect comparisons using a mixed treatment comparison based on a Bayesian hierarchical model. HRs and corresponding 95% credible intervals were calculated by Markov chain Monte Carlo methods using the “BUGSnet” package of R software (version 3.6.3; R Foundation, Vienna, Austria) [24]. Brooks-Gelman-Rubin plots method, trace plot and density plot were used to access the model convergence [25]. Besides, rank probabilities were calculated to obtain the hierarchical amount effects of multiple treatments. Given the graft type, CABG modalities and time period over which included studies were conducted, a random effects model was used. For the purposes of the mixed-treatment comparison, consistency in direct and indirect effects was assumed [26]. Heterogeneity between comparisons within the network was analyzed by examining I values for the random-effects model. Inconsistency was graphically examined using the BUGSnet nma.compare function to plot the individual data points’ posterior mean deviance contributions for the consistency model versus the inconsistency model [27]. Early mortality and need of interventional procedures and surgical re-interventions were reported as pooled prevalence of adverse outcome using the package “meta” of R software [28].