Estimation in the semiparametric frailty model with covariate measurement errors

Marginal partial likelihood approach is used for estimating the parameters in general frailty measurement error models when covariates are measured with error. An efficient algorithm based on Markov chain Monte Carlo stochastic approximation is proposed to solve the resulting estimating equations. Simulation studies show that the proposed estimation procedure work well and gives accurate estimates and their variance estimates. We also illustrate the method with a data set from the western Kenya parasitaemia data.