Year: 2015
Type: article
Abstract: Meta-analysis plays an important role in summarizing and synthesizing scientific evidence derived from multiple studies. With high-dimensional data, the incorporation of variable selection into meta-analysis improves model interpretation and prediction. Existing variable selection methods require direct access to raw data, which may not be available in practical situations. We propose a new approach, sparse meta-analysis (SMA), in which variable selection for meta-analysis is based solely on summary statistics and the effect sizes of each covariate are allowed to vary among studies. We show that the SMA enjoys the oracle property if the estimated covariance matrix of the parameter estimators from each study is available. We also show that our approach achieves selection consistency and estimation consistency even when summary statistics include only the variance estimators or no variance/covariance information at all. Simulation studies and applications to high-throughput genomics studies demonstrate the usefulness of our approach.
Source: Biostatistics
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: bronze
Awards: Statistical Methods in Chronic Disease Research, Statistical Methods for Cancer Clinical Trials, Collaborative Research: Semiparametric ODE Models for Complex Gene Regulatory Networks, Carolina Population Center, ABI Innovation: Gini-based methodologies to enhance network-scale transcriptome analysis in plants, Semiparametric Analysis of Censored Data in Current Medical Studies, Semiparametric Regression Analysis of Censored Data, G9000034599, G9000207646
OpenAlex ID: W2309927874