Robust likelihood-based survival modeling with microarray data

Hyung Jun Cho, Ami Yu, Sukwoo Kim, Jaewoo Kang, Seung Mo Hong

Research output: Contribution to journalArticle

16 Citations (Scopus)

Abstract

Gene expression data can be associated with various clinical outcomes. In particular, these data can be of importance in discovering survival-associated genes for medical applications. As alternatives to traditional statistical methods, sophisticated methods and software programs have been developed to overcome the high-dimensional difficulty of microarray data. Nevertheless, new algorithms and software programs are needed to include practical functions such as the discovery of multiple sets of survival-associated genes and the incorporation of risk factors, and to use in the R environment which many statisticians are familiar with. For survival modeling with microarray data, we have developed a software program (called rbsurv) which can be used conveniently and interactively in the R environment. This program selects survival-associated genes based on the partial likelihood of the Cox model and separates training and validation sets of samples for robustness. It can discover multiple sets of genes by iterative forward selection rather than one large set of genes. It can also allow adjustment for risk factors in microarray survival modeling. This software package, the rbsurv package, can be used to discover survival-associated genes with microarray data conveniently.

Original languageEnglish
JournalJournal of Statistical Software
Volume29
Issue number1
Publication statusPublished - 2009 Jan 1

Fingerprint

Microarrays
Microarray Data
Likelihood
Genes
Gene
Modeling
Risk Factors
Software
Partial Likelihood
Cox Model
Medical Applications
Medical applications
Gene Expression Data
Gene expression
Software Package
Microarray
Software packages
Large Set
Statistical method
Statistical methods

Keywords

  • Likelihood
  • Microarray data
  • R
  • Robustness
  • Survival data

ASJC Scopus subject areas

  • Software
  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Cite this

Robust likelihood-based survival modeling with microarray data. / Cho, Hyung Jun; Yu, Ami; Kim, Sukwoo; Kang, Jaewoo; Hong, Seung Mo.

In: Journal of Statistical Software, Vol. 29, No. 1, 01.01.2009.

Research output: Contribution to journalArticle

Cho, Hyung Jun ; Yu, Ami ; Kim, Sukwoo ; Kang, Jaewoo ; Hong, Seung Mo. / Robust likelihood-based survival modeling with microarray data. In: Journal of Statistical Software. 2009 ; Vol. 29, No. 1.
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