title: Detecting outlying trials in network meta-analysis.
abstract: Network meta-analysis (NMA) expands the scope of a conventional pairwise meta-analysis to simultaneously handle multiple treatment comparisons. However, some trials may appear to deviate markedly from the others and thus be inappropriate to be synthesized in the NMA. In addition, the inclusion of these trials in evidence synthesis may lead to bias in estimation. We call such trials trial-level outliers. To the best of our knowledge, while heterogeneity and inconsistency in NMA have been extensively discussed and well addressed, few previous papers have considered the proper detection and handling of trial-level outliers. In this paper, we propose several Bayesian outlier detection measures, which are then applied to a diabetes data set. Simulation studies comparing our approaches in both arm-based and contrast-based model settings are provided in two supporting appendices.
authors: Department of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, 20740, MD, U.S.A.
authors: Jing
authors: http://orcid.org/0000-0001-8126-3109
authors: J
authors: Zhang
authors: Eli Lilly and Company, Lilly Corporate Center, Indianapolis, 46285, IN, U.S.A.
authors: Haoda
authors: H
authors: Fu
authors: Division of Biostatistics, University of Minnesota School of Public Health, Minneapolis, 55455, MN, U.S.A.
authors: Bradley P
authors: BP
authors: Carlin
mesh_terms: D001499:Bayes Theorem; D015982:Bias; D002986:Clinical Trials as Topic / Q000379:methods / Q000706:statistics & numerical data*; D006801:Humans; D007004:Hypoglycemic Agents / Q000494:pharmacology; D015201:Meta-Analysis as Topic; D016011:Normal Distribution
chemical_list: D007004:Hypoglycemic Agents
keywords: detection measures; network meta-analysis; trial-level outliers