  The cause-specific cumulative incidence function (CIF) quantifies the
subject-specific disease risk with competing risk outcome. With longitudinally
collected biomarker data, it is of interest to dynamically update the predicted
CIF by incorporating the most recent biomarker as well as the cumulating
longitudinal history. Motivated by a longitudinal cohort study of chronic
kidney disease, we propose a framework for dynamic prediction of end stage
renal disease using multivariate longitudinal biomarkers, accounting for the
competing risk of death. The proposed framework extends the landmark survival
modeling to competing risks data, and implies that a distinct sub-distribution
hazard regression model is defined at each landmark time. The model parameters,
prediction horizon, longitudinal history and at-risk population are allowed to
vary over the landmark time. When the measurement times of biomarkers are
irregularly spaced, the predictor variable may not be observed at the time of
prediction. Local polynomial is used to estimate the model parameters without
explicitly imputing the predictor or modeling its longitudinal trajectory. The
proposed model leads to simple interpretation of the regression coefficients
and closed-form calculation of the predicted CIF. The estimation and prediction
can be implemented through standard statistical software with tractable
computation. We conducted simulations to evaluate the performance of the
estimation procedure and predictive accuracy. The methodology is illustrated
with data from the African American Study of Kidney Disease and Hypertension.
