Additive risk survival model with microarray data

Shuangge Ma; Jian Huang

doi:10.1186/1471-2105-8-192

Back

Additive risk survival model with microarray data

Journal article

Open access

Peer reviewed

Additive risk survival model with microarray data

Shuangge Ma and Jian Huang

BMC bioinformatics, Vol.8(1), pp.192-192

06/08/2007

DOI: 10.1186/1471-2105-8-192

PMCID: PMC1904459

PMID: 17559667

Files and links (1)

url

https://doi.org/10.1186/1471-2105-8-192View

Published (Version of record) Open Access

Abstract

Microarray techniques survey gene expressions on a global scale. Extensive biomedical studies have been designed to discover subsets of genes that are associated with survival risks for diseases such as lymphoma and construct predictive models using those selected genes. In this article, we investigate simultaneous estimation and gene selection with right censored survival data and high dimensional gene expression measurements. We model the survival time using the additive risk model, which provides a useful alternative to the proportional hazards model and is adopted when the absolute effects, instead of the relative effects, of multiple predictors on the hazard function are of interest. A Lasso (least absolute shrinkage and selection operator) type estimate is proposed for simultaneous estimation and gene selection. Tuning parameter is selected using the V-fold cross validation. We propose Leave-One-Out cross validation based methods for evaluating the relative stability of individual genes and overall prediction significance. We analyze the MCL and DLBCL data using the proposed approach. A small number of probes represented on the microarrays are identified, most of which have sound biological implications in lymphoma development. The selected probes are relatively stable and the proposed approach has overall satisfactory prediction power.

Risk Assessment - methods

Prognosis

Oligonucleotide Array Sequence Analysis - methods

Humans

Risk Factors

Proportional Hazards Models

Gene Expression Profiling - methods

Survival Rate

Neoplasm Proteins - metabolism

Incidence

Lymphoma, Mantle-Cell - mortality

Survival Analysis

Biomarkers, Tumor - metabolism

Lymphoma, Mantle-Cell - metabolism

Details

Title: Subtitle: Additive risk survival model with microarray data
Creators: Shuangge Ma - Department of Epidemiology and Public Health, Yale University, New Haven, CT 06520, USA. shuangge.ma@yale.edu
Jian Huang
Resource Type: Journal article
Publication Details: BMC bioinformatics, Vol.8(1), pp.192-192
Publisher: England
DOI: 10.1186/1471-2105-8-192
PMID: 17559667
PMCID: PMC1904459
ISSN: 1471-2105
eISSN: 1471-2105
Language: English
Date published: 06/08/2007
Academic Unit: Statistics and Actuarial Science
Record Identifier: 9983985948502771

Metrics

27 Record Views

28 Times Cited - Web of Science