Estimation and inference of predictive discrimination for survival outcome risk prediction models.

ABSTRACT: Accurate risk prediction has been the central goal in many studies of survival outcomes. In the presence of multiple risk factors, a censored regression model can be employed to estimate a risk prediction rule. Before the prediction tool can be popularized for practical use, it is crucial to rigorously assess its prediction performance. In our motivating example, researchers are interested in developing and validating a risk prediction tool to identify future lung cancer cases by integrating demographic information, disease characteristics and smoking-related data. Considering the long latency period of cancer, it is desirable for a prediction tool to achieve discriminative performance that does not weaken over time. We propose estimation and inferential procedures to comprehensively asses

SUBMITTER: Li R 

PROVIDER: S-EPMC10084512 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature