A plea for taking all available clinical information into account when assessing the predictive value of omics data.

ABSTRACT:

Background

Omics data can be very informative in survival analysis and may improve the prognostic ability of classical models based on clinical risk factors for various diseases, for example breast cancer. Recent research has focused on integrating omics and clinical data, yet has often ignored the need for appropriate model building for clinical variables. Medical literature on classical prognostic scores, as well as biostatistical literature on appropriate model selection strategies for low dimensional (clinical) data, are often ignored in the context of omics research. The goal of this paper is to fill this methodological gap by investigating the added predictive value of gene expression data for models using varying amounts of clinical information.

Methods

We analyze two

SUBMITTER: Volkmann A 

PROVIDER: S-EPMC6657034 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature