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Meta-analysis and sensitivity analysis for multi-arm trials with selection bias

Lookup NU author(s): Hathaikan Chootrakool, Dr Jian Shi


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Multi-arm trials meta-analysis is a methodology used in combining evidence based on a synthesis of different types of comparisons from all possible similar studies and to draw inferences about the effectiveness of multiple compared-treatments. Studies with statistically significant results are potentially more likely to be submitted and selected than studies with non-significant results; this leads to false-positive results. In meta-analysis, combining only the identified selected studies uncritically may lead to an incorrect, usually over-optimistic conclusion. This problem is known as selection bias. In this paper, we first define a random-effect meta-analysis model for multi-arm trials by allowing for heterogeneity among studies. This general model is based on a normal approximation for empirical log-odds ratio. We then address the problem of publication bias by using a sensitivity analysis and by defining a selection model to the available data of a meta-analysis. This method allows for different amounts of selection bias and helps to investigate how sensitive the main interest parameter is when compared with the estimates of the standard model. Throughout the paper, we use binary data from Antiplatelet therapy in maintaining vascular patency of patients to illustrate the methods. Copyright (C) 2011 John Wiley & Sons, Ltd.

Publication metadata

Author(s): Chootrakool H, Shi JQ, Yue RX

Publication type: Article

Publication status: Published

Journal: Statistics in Medicine

Year: 2011

Volume: 30

Issue: 11

Pages: 1183-1198

Print publication date: 16/01/2011

ISSN (print): 0277-6715

ISSN (electronic): 1097-0258

Publisher: John Wiley & Sons Ltd.


DOI: 10.1002/sim.4143


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Funder referenceFunder name
Suan Dusit Rajabhat University (SDU), Thailand
11zz116Shanghai Municipal Education Commission
E03004E-Institutes of Shanghai Municipal Education Commission
S30405Shanghai Leading Academic Discipline Project