Journal article
Semiparametric prognosis models in genomic studies
Briefings in bioinformatics, Vol.11(4), pp.385-393
07/2010
DOI: 10.1093/bib/bbp070
PMCID: PMC2905523
PMID: 20123942
Abstract
Development of high-throughput technologies makes it possible to survey the whole genome. Genomic studies have been extensively conducted, searching for markers with predictive power for prognosis of complex diseases such as cancer, diabetes and obesity. Most existing statistical analyses are focused on developing marker selection techniques, while little attention is paid to the underlying prognosis models. In this article, we review three commonly used prognosis models, namely the Cox, additive risk and accelerated failure time models. We conduct simulation and show that gene identification can be unsatisfactory under model misspecification. We analyze three cancer prognosis studies under the three models, and show that the gene identification results, prediction performance of all identified genes combined, and reproducibility of each identified gene are model-dependent. We suggest that in practical data analysis, more attention should be paid to the model assumption, and multiple models may need to be considered.
Details
- Title: Subtitle
- Semiparametric prognosis models in genomic studies
- Creators
- Shuangge Ma - School of Public Health, Yale University, USA. shuangge.ma@yale.eduJian HuangMingyu ShiYang LiBen-Chang Shia
- Resource Type
- Journal article
- Publication Details
- Briefings in bioinformatics, Vol.11(4), pp.385-393
- Publisher
- England
- DOI
- 10.1093/bib/bbp070
- PMID
- 20123942
- PMCID
- PMC2905523
- ISSN
- 1467-5463
- eISSN
- 1477-4054
- Grant note
- CA120988 / NCI NIH HHS LM009754 / NLM NIH HHS
- Language
- English
- Date published
- 07/2010
- Academic Unit
- Statistics and Actuarial Science
- Record Identifier
- 9983985965402771
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