Weighted Cox regression for the prediction of heterogeneous patient subgroups.

ABSTRACT:

Background

An important task in clinical medicine is the construction of risk prediction models for specific subgroups of patients based on high-dimensional molecular measurements such as gene expression data. Major objectives in modeling high-dimensional data are good prediction performance and feature selection to find a subset of predictors that are truly associated with a clinical outcome such as a time-to-event endpoint. In clinical practice, this task is challenging since patient cohorts are typically small and can be heterogeneous with regard to their relationship between predictors and outcome. When data of several subgroups of patients with the same or similar disease are available, it is tempting to combine them to increase sample size, such as in multicenter studies. How

SUBMITTER: Madjar K 

PROVIDER: S-EPMC8650299 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature