Evaluating Discrimination of a Lung Cancer Risk Prediction Model Using Partial Risk-Score in a Two-Phase Study.

ABSTRACT:

Background

Independent validation of risk prediction models in prospective cohorts is required for risk-stratified cancer prevention. Such studies often have a two-phase design, where information on expensive biomarkers are ascertained in a nested substudy of the original cohort.

Methods

We propose a simple approach for evaluating model discrimination that accounts for incomplete follow-up and gains efficiency by using data from all individuals in the cohort irrespective of whether they were sampled in the substudy. For evaluating the AUC, we estimated probabilities of risk-scores for cases being larger than those in controls conditional on partial risk-scores, computed using partial covariate information. The proposed method was compared with an inverse probability weighted

SUBMITTER: Pal Choudhury P 

PROVIDER: S-EPMC11807412 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature