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A selection model for accounting for publication bias in a full network meta‐analysis
Author(s) -
Mavridis Dimitris,
Welton Nicky J.,
Sutton Alex,
Salanti Georgia
Publication year - 2014
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.6321
Subject(s) - frequentist inference , model selection , computer science , selection (genetic algorithm) , sensitivity (control systems) , selection bias , consistency (knowledge bases) , bayesian probability , maximization , bayesian network , publication bias , econometrics , meta analysis , statistics , bayesian inference , machine learning , artificial intelligence , mathematics , confidence interval , mathematical optimization , medicine , electronic engineering , engineering
Copas and Shi suggested a selection model to explore the potential impact of publication bias via sensitivity analysis based on assumptions for the probability of publication of trials conditional on the precision of their results. Chootrakool et al . extended this model to three‐arm trials but did not fully account for the implications of the consistency assumption, and their model is difficult to generalize for complex network structures with more than three treatments. Fitting these selection models within a frequentist setting requires maximization of a complex likelihood function, and identification problems are common. We have previously presented a Bayesian implementation of the selection model when multiple treatments are compared with a common reference treatment. We now present a general model suitable for complex, full network meta‐analysis that accounts for consistency when adjusting results for publication bias. We developed a design‐by‐treatment selection model to describe the mechanism by which studies with different designs (sets of treatments compared in a trial) and precision may be selected for publication. We fit the model in a Bayesian setting because it avoids the numerical problems encountered in the frequentist setting, it is generalizable with respect to the number of treatments and study arms, and it provides a flexible framework for sensitivity analysis using external knowledge. Our model accounts for the additional uncertainty arising from publication bias more successfully compared to the standard Copas model or its previous extensions. We illustrate the methodology using a published triangular network for the failure of vascular graft or arterial patency. Copyright © 2014 John Wiley & Sons, Ltd.