Two-stage sampling for better survival model performance.

ABSTRACT:

Background

With the emergence of high-dimensional censored survival data in health and medicine, the use of survival models for risk prediction is increasing. To date, practical techniques exist for splitting data for model training and performance evaluation. While different sampling methods have been compared for their performances, the effect of data splitting ratio and survival specific characteristics have not yet been examined for high dimensional censored survival data.

Methods

We first conduct an empirical study of using the simple random sampling technique and stratified sampling technique on real high-dimensional gene expression datasets Lasso Cox model performance. For the simple random sampling technique, various data splitting ratios are investigated. For the st

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC12570514 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature