Semiparametric prognosis models in genomic studies.

Briefings in Bioinformatics
Shuangge MaBen-Chang Shia

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.

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Citations

Nov 20, 2010·Briefings in Bioinformatics·Shuangge Ma, Xiao Song
Jan 20, 2011·Cancer Informatics·Xuesong HanShuangge Ma
Nov 8, 2011·Expert Opinion on Medical Diagnostics·Yawei ZhangShuangge Ma
Feb 22, 2012·Methods of Information in Medicine·S MaN Yi
Feb 18, 2011·Biometrical Journal. Biometrische Zeitschrift·Harald BinderMartin Schumacher

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