Journal article
PENALIZED INTEGRATIVE ANALYSIS UNDER THE ACCELERATED FAILURE TIME MODEL
Statistica Sinica, Vol.26(2), pp.493-508
04/01/2016
DOI: 10.5705/ss.2014.194
Abstract
For survival data with high-dimensional covariates, results generated in the analysis of a single dataset are often unsatisfactory because of the small sample size. Integrative analysis pools raw data from multiple independent studies with comparable designs, effectively increases sample size, and has better performance than meta-analysis and single-dataset analysis. In this study, we conduct integrative analysis of survival data under the accelerated failure time (AFT) model. The sparsity structures of multiple datasets are described using homogeneity and heterogeneity models. For variable selection under the homogeneity model, we adopt group penalization approaches; for variable selection under the heterogeneity model, we use composite penalization and sparse group penalization approaches. As a major advancement from existing studies, the asymptotic selection and estimation properties are rigorously established. Simulation study is conducted to compare different penalization methods and against alternatives. We also analyze four lung cancer prognosis datasets with gene expression measurements.
Details
- Title: Subtitle
- PENALIZED INTEGRATIVE ANALYSIS UNDER THE ACCELERATED FAILURE TIME MODEL
- Creators
- Qingzhao Zhang - Chinese Academy of SciencesSanguo Zhang - Chinese Academy of SciencesJin Liu - National University of SingaporeJian Huang - University of IowaShuangge Ma - Yale University
- Resource Type
- Journal article
- Publication Details
- Statistica Sinica, Vol.26(2), pp.493-508
- Publisher
- STATISTICA SINICA
- DOI
- 10.5705/ss.2014.194
- ISSN
- 1017-0405
- eISSN
- 1996-8507
- Number of pages
- 16
- Grant note
- 13ZD148; 13CTJ001 / National Social Science Foundation of China 11401561; 71471152; 71201139; 71301162 / National Natural Science Foundation of China R03CA182984; P30CA016359; R01CA142774 / NIH
- Language
- English
- Date published
- 04/01/2016
- Academic Unit
- Statistics and Actuarial Science
- Record Identifier
- 9984257630402771
Metrics
5 Record Views