A new semiparametric estimation method for accelerated hazard model

The accelerated hazard model has been proposed for more than a decade. However, its application is still very limited, partly due to the complexity of the existing semiparametric estimation method. We propose a new semiparametric estimation method based on a kernel-smoothed approximation to the limit of a profile likelihood function of the model. The method leads to smooth estimating equations and is easy to use. The estimates from the method are proved to be consistent and asymptotically normal. Our numerical study shows that the new method is more efficient than the existing method. The proposed method is employed to reanalyze the data from a brain tumor treatment study.