Aim: This meta-analysis aimed to pool available data regarding the efficacy of ganciclovir treatment among cytomegalovirus-detected ulcerative colitis patients. Methods: We screened PubMed, Ovid, Web of Science and Cochrane databases for relevant studies, and four investigators independently evaluated the studies for eligibility. The primary outcome was surgical resection or death from ulcerative colitis. The data were then pooled via DerSimonian-Laird estimator and Mantel-Haenszel (MH) method, two points added for continuity correction and random-effects model fitted in the Bayesian framework. We first constructed a Bugs model with Student t-distribution as prior for between-study heterogeneity. The model was fitted by Gibbs sampler (JAGS) to produce a marginal posterior distribution. Results: Our screening identified 15 eligible studies for final data synthesis and combined data from 191 ganciclovir-treated and 166 non-treated patients. Effect estimates from the fixed-effects meta-analysis model did not encourage ganciclovir treatment (OR, 1.43; 95% CIs [0-95, 2.16]), with a negligible unaccounted heterogeneity (I2 = 0%). The Bayesian random-effects model generated high-density credible intervals, suggesting a high probability, that future studies will also not encourage ganciclovir treatment (mu, 1.028; 95% credible intervals [0.054, 2.238]; 80% credible intervals [0.401, 1.703]) which indicates that future studies will favor non-treatment of ulcerative colitis with ganciclovir.Conclusions: Data produced in this study do not encourage ganciclovir treatment for UC patients. However, studies included in this analysis were observational, and thus, inherited severe selection bias. We suggest randomized controlled studies be conducted to make firm recommendations in this context.
Aim: This meta-analysis aimed to pool available data regarding the efficacy of ganciclovir treatment among cytomegalovirus-detected ulcerative colitispatients. Methods: We screened PubMed, Ovid, Web of Science and Cochrane databases for relevant studies, and four investigators independently evaluated the studies for eligibility. The primary outcome was surgical resection or death from ulcerative colitis. The data were then pooled via DerSimonian-Laird estimator and Mantel-Haenszel (MH) method, two points added for continuity correction and random-effects model fitted in the Bayesian framework. We first constructed a Bugs model with Student t-distribution as prior for between-study heterogeneity. The model was fitted by Gibbs sampler (JAGS) to produce a marginal posterior distribution. Results: Our screening identified 15 eligible studies for final data synthesis and combined data from 191 ganciclovir-treated and 166 non-treated patients. Effect estimates from the fixed-effects meta-analysis model did not encourage ganciclovir treatment (OR, 1.43; 95% CIs [0-95, 2.16]), with a negligible unaccounted heterogeneity (I2 = 0%). The Bayesian random-effects model generated high-density credible intervals, suggesting a high probability, that future studies will also not encourage ganciclovir treatment (mu, 1.028; 95% credible intervals [0.054, 2.238]; 80% credible intervals [0.401, 1.703]) which indicates that future studies will favor non-treatment of ulcerative colitis with ganciclovir.Conclusions: Data produced in this study do not encourage ganciclovir treatment for UC patients. However, studies included in this analysis were observational, and thus, inherited severe selection bias. We suggest randomized controlled studies be conducted to make firm recommendations in this context.