2.6 Statistical analysis
Treatment nodes included different routes of administration. We drew network plots with the multinmapackage in R (version 4.1.3).18 We conducted the network meta-analysis using a random-effects model and consistency model. This analysis was performed with the Bayesian framework.19 We chose mean differences (MD) and 95% credible intervals (CrI) for intraocular pressure. We used the Markov chain Monte Carlo method, which built up four chains, and set 80,000 iterations after an initial burn-in of 20,000 and a thinning of one. We assessed local incoherence and obtained indirect estimates using node splitting models.20 We calculated the surface under the cumulative ranking curve (SUCRA) to rank different administration routes.21 SUCRA is a percentage interpreted as the probability of a treatment that is the safest without uncertainty on the outcome, which is equal to 1 or 0 when the treatment is certain to be the best or the worst respectively. We performed multiple sensitivity analyses: 1) exclusion of studies without diabetes mellitus; 2) exclusion of studies with fewer than 20 participants; and 3) exclusion of studies with combined laser therapy. We conducted the above statistical analyses using the gemtcpackage in R (Version 4.1.3).