Taipei: Academia Sinica * Institute of Statistical Science
Abstract:
Owing to the fact that general semiparametric inference procedures are
still underdeveloped for multivariate interval-censored event time data, we propose
semiparametric maximum likelihood estimation for the gamma-frailty Cox
model under mixed-case interval censoring. We establish the consistency of the
semiparametric maximum likelihood estimator (SPMLE) for the model parameters,
including the regression coefficients and the cumulative hazard functions in
the Cox model, and the variance of the gamma frailty. The SPMLEs of the cumulative
hazard functions are shown to have a n
1/3
-rate of convergence, while those of
the regression coefficients and the frailty variance have a n
1/2
-rate of convergence;
here n denotes the number of study units. The asymptotic normality of the regression
coefficients and the frailty variance is also established, with the asymptotic
variance given by the inverse of the efficient Fisher information matrix. A profilelikelihood
approach is proposed for estimating the asymptotic variance. Based on
the self-consistency equations and the contraction principle, we propose a stable
and efficient computation algorithm. Simulation results reveal that the large sample
theories work quite well in finite samples. We analyze a dataset from an AIDS
clinical trial by the proposed methods to assess the effects of the baseline CD4 cell
counts on the times to CMV shedding in blood and urine.