High-Dimensional Variable Selection in Meta-Analysis for Censored Data
Article first published online: 5 AUG 2010
© 2010, The International Biometric Society
Volume 67, Issue 2, pages 504–512, June 2011
How to Cite
Liu, F., Dunson, D. and Zou, F. (2011), High-Dimensional Variable Selection in Meta-Analysis for Censored Data. Biometrics, 67: 504–512. doi: 10.1111/j.1541-0420.2010.01466.x
- Issue published online: 20 JUN 2011
- Article first published online: 5 AUG 2010
- Received December 2008. Revised February 2010. Accepted May 2010.
- Accelerated failure time;
- Expectation maximization (EM) algorithm;
- Maximum a posteriori (MAP) estimation;
- Relevance vector machine;
Summary This article considers the problem of selecting predictors of time to an event from a high-dimensional set of candidate predictors using data from multiple studies. As an alternative to the current multistage testing approaches, we propose to model the study-to-study heterogeneity explicitly using a hierarchical model to borrow strength. Our method incorporates censored data through an accelerated failure time model. Using a carefully formulated prior specification, we develop a fast approach to predictor selection and shrinkage estimation for high-dimensional predictors. For model fitting, we develop a Monte Carlo expectation maximization (MC-EM) algorithm to accommodate censored data. The proposed approach, which is related to the relevance vector machine (RVM), relies on maximum a posteriori estimation to rapidly obtain a sparse estimate. As for the typical RVM, there is an intrinsic thresholding property in which unimportant predictors tend to have their coefficients shrunk to zero. We compare our method with some commonly used procedures through simulation studies. We also illustrate the method using the gene expression barcode data from three breast cancer studies.