Genomic biomarkers for a binary clinical outcome in early drug development microarray experiments.
In this article, we discuss methods to select three different types of genes (treatment related, response related, or both) and investigate whether they can serve as biomarkers for a binary outcome variable. We consider an extension of the joint model introduced by Lin et al. (2010) and Tilahun et al. (2010) for a continuous response. As the model has certain drawbacks in a binary setting, we also present a way to use classical selection methods to identify subgroups of genes, which are treatment and/or response related. We evaluate their potential to serve as biomarkers by applying DLDA to predict the response level.
Johnson & Johnson, PRD, Beerse, Belgium. svsande1@its.jnj.com
Suzy
S
Van Sanden
Ziv
Z
Shkedy
Tomasz
T
Burzykowski
Hinrich W H
HW
Göhlmann
Willem
W
Talloen
Luc
L
Bijnens
D000818:Animals; D015415:Biomarkers; D055808:Drug Discovery / Q000379:methods*; D005819:Genetic Markers / Q000235:genetics*; D023281:Genomics / Q000379:methods*; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D013997:Time Factors; D016896:Treatment Outcome
D015415:Biomarkers; D005819:Genetic Markers