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Meta‐analysis and sensitivity analysis for multi‐arm trials with selection bias
Author(s) -
Chootrakool Hathaikan,
Shi Jian Qing,
Yue Rongxian
Publication year - 2011
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.4143
Subject(s) - sensitivity (control systems) , selection bias , selection (genetic algorithm) , statistics , computer science , meta analysis , econometrics , medicine , mathematics , artificial intelligence , electronic engineering , engineering
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 asbiselection 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 © 2011 John Wiley & Sons, Ltd.