Improving estimation efficiency for multivariate failure time data with auxiliary covariates

For the analysis of multivariate failure time data when the primary covariates are incomplete but the auxiliary covariates for them are available for the whole cohort subjects, an estimated quadratic inference function method is developed to improve the estimation efficiency under the marginal hazard model with common baseline hazard function. Both the auxiliary information and the intra-cluster correlation between the failure times are incorporated in the estimation procedure. The proposed estimator is shown to be consistent and asymptotically normal. Simulation studies demonstrate that when the intra-cluster correlation is strong or moderate, the proposed method gains noticeable efficiency compared to other existing methods.

Automated summary

An estimated quadratic inference function method is developed for the analysis of multivariate failure time data, where the primary covariates are incomplete but the auxiliary covariates for them are available for the whole cohort subjects. This method improves the estimation efficiency under the marginal hazard model with common baseline hazard function by incorporating both the auxiliary information and the intra-cluster correlation between the failure times. Simulation studies demonstrate that the proposed method gains noticeable efficiency compared to other existing methods when the intra-cluster correlation is strong or moderate.