Comparative effectiveness of antiviral treatment for hepatitis B: a systematic review and Bayesian network meta-analysis.
OBJECTIVE
A wide variety of competing drugs are available to patients for the treatment of chronic hepatitis B. We update a recent meta-analysis to include additional trial evidence with the aim of determining which treatment is the most effective.


METHODS
Twelve monotherapy or combination therapy were evaluated in treatment-naive individuals with hepatitis B e antigen (HBeAg) positive or negative patients. Databases were searched for randomized controlled trials in the first year of therapy. Bayesian random effects network meta-analysis was used to calculate the pairwise odds ratios, 95% credible intervals and ranking of six surrogate outcomes.


RESULTS
In total, 22 studies were identified (7508 patients): 12 studies analysed HBeAg-positive patients, six analysed HBeAg-negative patients, and four evaluated both. Tenofovir was most effective at increasing efficacy in HBeAg-positive patients, ranking first for three outcomes and increased odds of undetectable levels of hepatitis B virus (HBV) DNA compared with seven other therapies (such as lamivudine: odds ratio 33.0; 95% credible interval 7.0-292.7). For HBeAg-negative patients, the large network (seven therapies) ranked entecavir alone or in combination with tenofovir highly for reduction in HBV DNA and histologic improvement. In the smaller network (three therapies), tenofovir ranked first for undetectable HBV DNA and histologic improvement. No data existed to directly or indirectly compare these treatments.


CONCLUSIONS
For HBeAg-positive patients tenofovir is the most effective at increasing efficacy, whereas for HBeAg-negative patients, either tenofovir or entecavir is most effective. Further research should focus on strengthening the network connections, in particular comparing tenofovir and entecavir in HBeAg-negative patients.
aHealth Economics and Health Technology Assessment, Institute of Health and Wellbeing, University of Glasgow bSchool of Health & Life Sciences, Glasgow Caledonian University, Glasgow cOxford Outcomes, Oxford, UK.
Lindsay
L
Govan
Olivia
O
Wu
Yiqiao
Y
Xin
Sharon J
SJ
Hutchinson
Neil
N
Hawkins
D000998:Antiviral Agents / Q000009:adverse effects / Q000191:economics* / Q000627:therapeutic use*; D001499:Bayes Theorem; D015415:Biomarkers / Q000097:blood; D003362:Cost-Benefit Analysis; D004279:DNA, Viral / Q000097:blood; D016527:Drug Costs; D004359:Drug Therapy, Combination; D006513:Hepatitis B e Antigens / Q000097:blood; D006515:Hepatitis B virus / Q000187:drug effects* / Q000235:genetics / Q000276:immunology; D019694:Hepatitis B, Chronic / Q000175:diagnosis / Q000188:drug therapy* / Q000191:economics*; D006801:Humans; D008390:Markov Chains; D009010:Monte Carlo Method; D016017:Odds Ratio; D013997:Time Factors; D016896:Treatment Outcome; D019562:Viral Load
D000998:Antiviral Agents; D015415:Biomarkers; D004279:DNA, Viral; D006513:Hepatitis B e Antigens