Online Program Home
My Program

Abstract Details

Activity Number: 70
Type: Contributed
Date/Time: Sunday, July 31, 2016 : 4:00 PM to 5:50 PM
Sponsor: Biometrics Section
Abstract #319853
Title: Detecting Publication Bias in Multivariate Random-Effects Meta-Analysis
Author(s): Chuan Hong* and Haitao Chu and Yong Chen
Companies: The University of Texas Health Science Center at Houston and University of Minnesota and University of Pennsylvania Perelman School of Medicine
Keywords: Comparative effectiveness research ; Composite likelihood ; Outcome reporting bias ; Systematic review

Publication bias occurs when the publication of research results depends not only on the quality of the research but also on the direction, magnitude, or statistical significance of the results. The consequence is that published studies may not represent all valid studies undertaken, and this bias may threaten the validity of systematic reviews and meta-analyses. However, both detecting and accounting for publication bias are challenging in a multivariate meta-analysis setting because some studies may be completely unpublished while others may selectively report only part of multiple outcomes. In this paper, we propose a pseudolikelihood-based score test for detecting publication bias in multivariate random-effects meta-analysis. To the best of our knowledge, this is the first test for detecting publication bias in a multivariate meta-analysis setting. Two detailed case studies are given to show the limitations of univariate tests and to illustrate the advantage of the proposed test in practice. Through simulation studies, the proposed test is found to be more powerful than the existing univariate tests.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2016 program

Copyright © American Statistical Association