  A popular Bayesian nonparametric approach to survival analysis consists in
modeling hazard rates as kernel mixtures driven by a completely random measure.
In this paper we derive asymptotic results for linear and quadratic functionals
of such random hazard rates. In particular, we prove central limit theorems for
the cumulative hazard function and for the path-second moment and path-variance
of the hazard rate. Our techniques are based on recently established criteria
for the weak convergence of single and double stochastic integrals with respect
to Poisson random measures. We illustrate our results by considering specific
models involving kernels and random measures commonly exploited in practice.
