Bayesian network meta-analysis
Hepatitis B virus (HBV) infection is among the most common persistent viral infections in humans. Chronic infection of HBV (CHB) can lead to serious medical complications such as cirrhosis, hepatocellular carcinoma, and liver failure. Surrogate markers are often used to monitor the progression of the disease and patients are treated with antiviral agents to reduce the risk of developing complications. A wide variety of competing drugs are available to patients but measuring how effective these treatments are can be problematic. The only network meta-analysis in this area had a several methodological limitations, reducing the confidence in the results produced. The systematic review was updated with additional trials, and additional methodological requirements accounted for in the network. The aim was to determine which treatment is the most effective in treating CHB patients by analysing surrogate outcomes in CHB.
A Bayesian network meta-analysis (NMA) was performed to explore effectiveness of twelve mono- or combination therapies in HBeAg-positive and negative patients. Therapies include: placebo (PLA), lamivudine (LAM), pegylated interferon (PEG), adefovir (ADV), entecavir (ETV), telbivudine (LdT), and tenofovir (TDF). Odds ratios, 95% credible intervals and ranking were calculated for six surrogate outcomes. Three-arm trials were included, and effect estimates were adjusted to account for the correlation between each pair of arms.
Data from 22 studies (7508 patients) were included. For HBeAg-positive patients, Tenofovir was most effective at increasing efficacy, ranking first for three outcomes. For HBeAg-negative patients, the network was disconnected and analysed as 2 separate networks. Tenofovir or entecavir is most effective at increasing efficacy.
Figure: Network of trial comparisons for (a) chronic HBeAg-positive, and (b) chronic HBeAg-negative patients. Numbers represent that number of direct comparisons available. Total number of comparisons is greater than number of trials because of the three-arm trials.