Assessing the calibration of subdistribution hazard models in discrete time. (English. French summary) Zbl 1492.62167
Summary: 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, we propose methods for assessing the calibration of discrete time-to-event models in the presence of competing risks. Specifically, we consider the class of discrete subdistribution hazard models, which directly relate the cumulative incidence function of one event of interest to a set of covariates. We apply the methods to a prediction model for the development of nosocomial pneumonia. 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.
MSC:
| 62P10 | Applications of statistics to biology and medical sciences; meta analysis |
| 62N01 | Censored data models |
Keywords:
calibration; competing risks; discrete time-to-event data; subdistribution hazard; validationReferences:
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