  The generalization performance of a risk prediction model can be evaluated by
its calibration, which measures the agreement between predicted and observed
outcomes on external validation data. Here, methods for assessing the
calibration of discrete time-to-event models in the presence of competing risks
are proposed. The methods are designed for the class of discrete
subdistribution hazard models, which directly relate the cumulative incidence
function of one event of interest to a set of covariates. Simulation studies
show that the methods are strong tools for calibration assessment even in
scenarios with a high censoring rate and/or a large number of discrete time
points. The proposed approaches are illustrated by an analysis of nosocomial
pneumonia.
