Semiparametric prognosis models in genomic studies

Shuangge Ma, Jian Huang, Mingyu Shi, Yang Li, Ben Chang Shia

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)


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 prog-nosis 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.

Original languageEnglish
Article numberbbp070
Pages (from-to)385-393
Number of pages9
JournalBriefings in Bioinformatics
Issue number4
Publication statusPublished - Feb 1 2010
Externally publishedYes


  • Genomic studies
  • Model comparison
  • Semiparametric prognosis models

ASJC Scopus subject areas

  • Information Systems
  • Molecular Biology


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