  We introduce a flexible individual frailty model for clustered right-censored
data, in which covariate effects can be marginally interpreted as log failure
odds ratios. Flexible correlation structures can be imposed by introducing
multivariate exponential distributed frailties, constructed from a set of
multivariate Gaussian random variables. Finite and infinite dimensional
parameters are consistently estimated by maximizing a composite contributing
marginal likelihood and a consistent estimate for their asymptotic covariance
is proposed. Parameter estimation is implemented through a hybrid
expectation-maximum algorithm. Simulations and an analysis of the Rats study
were carried out to demonstrate our method.
