Assumption Weighting for Incorporating Heterogeneity Into Meta-Analysis of Genomic Data

• Activity Number: 242
• Type: Contributed
• Date/Time: Monday, July 30, 2012 : 2:00 PM to 3:50 PM
• Sponsor: Biometrics Section
• Author(s): Yihan Li*+ and Debashis Ghosh
• Companies: Penn State University and Penn State University
• Keywords: Meta-analysis ; Multiple testing ; Heterogeneity ; Microarray data analysis

Abstract

There is now a large literature on statistical methods for the meta-analysis of genomic data from multiple studies. However, a crucial assumption for performing many of these analyses is that the data exhibit small between-study variation or that this heterogeneity can be sufficiently modelled probabilistically. In this article, we propose "assumption weighting," which exploits a weighted hypothesis testing framework proposed by Genovese et al. (2006, Biometrika 93, 506 - 524) to incorporate tests of between-study variation into the meta-analysis context. This methodology is fast and computationally simple to implement. Several weighting schemes are considered and compared using simulation studies. In addition, we illustrate application of the proposed methodology using data from several high-profile stem cell gene expression datasets.