  An individualized risk prediction model that dynamically updates the
probability of a clinical event from a specific cause is valuable for
physicians to be able to optimize personalized treatment strategies in
real-time by incorporating all available information collected over the
follow-up. However, this is more complex and challenging when competing risks
are present, because it requires simultaneously updating the overall survival
and the cumulative incidence functions (CIFs) while adjusting for the
time-dependent covariates and time-varying covariate effects. In this study, we
developed a landmark proportional subdistribution hazards (PSH) model and a
more comprehensive supermodel by extending the landmark method to the Fine-Gray
model. The performance of our models was assessed via simulations and through
analysis of data from a multicenter clinical trial for breast cancer patients.
Our proposed models have appealing advantages over other dynamic prediction
models for data with competing risks. First, our models are robust against
violations of the PSH assumption and can directly predict the conditional CIFs
bypassing the estimation of overall survival and greatly simplify the
prediction procedure. Second, our landmark PSH supermodel enables researchers
to make predictions at a set of landmark points in one step. Third, the
proposed models can easily incorporate various types of time-dependent
information using existing standard software without computational burden.
