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Dynamic prediction of cumulative incidence functions by direct binomial regression
Author(s) -
Grand Mia K.,
de Witte Theo J. M.,
Putter Hein
Publication year - 2018
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.201700194
Subject(s) - censoring (clinical trials) , negative binomial distribution , cumulative incidence , covariate , computer science , statistics , regression analysis , cumulative distribution function , binomial regression , regression , econometrics , mathematics , probability density function , cohort , poisson distribution
In recent years there have been a series of advances in the field of dynamic prediction. Among those is the development of methods for dynamic prediction of the cumulative incidence function in a competing risk setting. These models enable the predictions to be updated as time progresses and more information becomes available, for example when a patient comes back for a follow‐up visit after completing a year of treatment, the risk of death, and adverse events may have changed since treatment initiation. One approach to model the cumulative incidence function in competing risks is by direct binomial regression, where right censoring of the event times is handled by inverse probability of censoring weights. We extend the approach by combining it with landmarking to enable dynamic prediction of the cumulative incidence function. The proposed models are very flexible, as they allow the covariates to have complex time‐varying effects, and we illustrate how to investigate possible time‐varying structures using Wald tests. The models are fitted using generalized estimating equations. The method is applied to bone marrow transplant data and the performance is investigated in a simulation study.