Identifying statistically significant combinatorial markers for survival analysis.

ABSTRACT:

Background

Survival analysis methods have been widely applied in different areas of health and medicine, spanning over varying events of interest and target diseases. They can be utilized to provide relationships between the survival time of individuals and factors of interest, rendering them useful in searching for biomarkers in diseases such as cancer. However, some disease progression can be very unpredictable because the conventional approaches have failed to consider multiple-marker interactions. An exponential increase in the number of candidate markers requires large correction factor in the multiple-testing correction and hide the significance.

Methods

We address the issue of testing marker combinations that affect survival by adapting the recently developed Limitles

SUBMITTER: Relator RT 

PROVIDER: S-EPMC5918465 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature