Internal validation strategy for high dimensional prognosis model: A simulation study and application to transcriptomic in head and neck tumors.

ABSTRACT:

Background

Predictive models using high-dimensional data, such as genomics and transcriptomics, are increasingly used in oncology for time-to-event endpoints. Internal validation of these models is crucial to mitigate optimism bias prior to external validation. Common strategies include train-test, bootstrap, and (nested) cross-validation. However, no benchmark exists for these methods in high-dimensional settings. We aimed to compare these strategies and provide recommendations in the field of transcriptomic analysis.

Method

A simulation study was conducted using data from the SCANDARE head and neck cohort (NCT03017573) including n = 76 patients. Simulated datasets included clinical variables (age, sex, HPV status, TNM staging), transcriptomic data (15,000 transcripts), and

SUBMITTER: Dubray-Vautrin A 

PROVIDER: S-EPMC12451366 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature