Testing Calibration of Cox Survival Models at Extremes of Event Risk.

ABSTRACT: Risk prediction models can translate genetic association findings for clinical decision-making. Most models are evaluated on their ability to discriminate, and the calibration of risk-prediction models is largely overlooked in applications. Models that demonstrate good discrimination in training datasets, if not properly calibrated to produce unbiased estimates of risk, can perform poorly in new patient populations. Poorly calibrated models arise due to missing covariates, such as genetic interactions that may be unknown or not measured. We demonstrate that models omitting interactions can lead to increased bias in predicted risk for patients at the tails of the risk distribution; i.e., those patients who are most likely to be affected by clinical decision making. We propose a new calibrat

SUBMITTER: Soave DM 

PROVIDER: S-EPMC5972303 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature