Network meta-analysis of randomized clinical trials: reporting the proper summaries.

TitleNetwork meta-analysis of randomized clinical trials: reporting the proper summaries.
Publication TypeJournal Article
Year of Publication2014
AuthorsZhang, Jing, Bradley P. Carlin, James D. Neaton, Guoxing G. Soon, Lei Nie, Robert Kane, Beth A. Virnig, and Haitao Chu
JournalClin Trials
Volume11
Issue2
Pagination246-62
Date Published2014 Apr
ISSN1740-7753
KeywordsBayes Theorem, Humans, Meta-Analysis as Topic, Odds Ratio, Randomized Controlled Trials as Topic
Abstract

BACKGROUND: In the absence of sufficient data directly comparing multiple treatments, indirect comparisons using network meta-analyses (NMAs) can provide useful information. Under current contrast-based (CB) methods for binary outcomes, the patient-centered measures including the treatment-specific event rates and risk differences (RDs) are not provided, which may create some unnecessary obstacles for patients to comprehensively trade-off efficacy and safety measures.PURPOSE: We aim to develop NMA to accurately estimate the treatment-specific event rates.METHODS: A Bayesian hierarchical model is developed to illustrate how treatment-specific event rates, RDs, and risk ratios (RRs) can be estimated. We first compare our approach to alternative methods using two hypothetical NMAs assuming a fixed RR or RD, and then use two published NMAs to illustrate the improved reporting.RESULTS: In the hypothetical NMAs, our approach outperforms current CB NMA methods in terms of bias. In the two published NMAs, noticeable differences are observed in the magnitude of relative treatment effects and several pairwise statistical significance tests from previous report.LIMITATIONS: First, to facilitate the estimation, each study is assumed to hypothetically compare all treatments, with unstudied arms being missing at random. It is plausible that investigators may have selected treatment arms on purpose based on the results of previous trials, which may lead to 'nonignorable missingness' and potentially bias our estimates. Second, we have not considered methods to identify and account for potential inconsistency between direct and indirect comparisons.CONCLUSIONS: The proposed NMA method can accurately estimate treatment-specific event rates, RDs, and RRs and is recommended.

DOI10.1177/1740774513498322
Alternate JournalClin Trials
Original PublicationNetwork meta-analysis of randomized clinical trials: reporting the proper summaries.
PubMed ID24096635
PubMed Central IDPMC3972291
Grant ListP30 CA077598 / CA / NCI NIH HHS / United States
R01 CA157458 / CA / NCI NIH HHS / United States
1P01CA142538 / CA / NCI NIH HHS / United States
R24 HD041041 / HD / NICHD NIH HHS / United States
AI103012 / AI / NIAID NIH HHS / United States
P01 CA142538 / CA / NCI NIH HHS / United States
R21 AI103012 / AI / NIAID NIH HHS / United States
1R01-CA157458-01A1 / CA / NCI NIH HHS / United States
Project: