Regression for censored survival data with lag effects

We consider regression modeling of survival data subject to right censoring when the full effect of some covariates (e.g. treatment) may be delayed. Several models are proposed, and methods for computing the maximum likelihood estimates of the parameters are described. Consistency and asymptotic normality properties of the estimators are derived. Some numerical examples are used to illustrate the implementation of the modeling and estimation procedures. Finally we apply the theory to interim data from a large scale randomized clinical trial for the prevention of skin cancer.