  Competing risk data appear widely in modern biomedical research.
Cause-specific hazard models are often used to deal with competing risk data in
the past two decades. There is no current study on the kernel likelihood method
for the cause-specific hazard model with time-varying coefficients. We propose
to use the local partial log-likelihood approach for nonparametric time-varying
coefficient estimation. Simulation studies demonstrate that our proposed
nonparametric kernel estimator has a good performance under assumed finite
sample settings. Finally, we apply the proposed method to analyze a diabetes
dialysis study with competing death causes.
