Graphical calibration curves and the integrated calibration index (ICI) for competing risk models.

ABSTRACT:

Background

Assessing calibration-the agreement between estimated risk and observed proportions-is an important component of deriving and validating clinical prediction models. Methods for assessing the calibration of prognostic models for use with competing risk data have received little attention.

Methods

We propose a method for graphically assessing the calibration of competing risk regression models. Our proposed method can be used to assess the calibration of any model for estimating incidence in the presence of competing risk (e.g., a Fine-Gray subdistribution hazard model; a combination of cause-specific hazard functions; or a random survival forest). Our method is based on using the Fine-Gray subdistribution hazard model to regress the cumulative incidence function of

SUBMITTER: Austin PC 

PROVIDER: S-EPMC8762819 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature