Additive hazards regression for case-cohort studies

The case-cohort design is a common means of reducing cost in large epidemiological cohort studies. Under this design, covariates are measured only on the cases and a subcohort randomly selected from the entire cohort. In this paper, we demonstrate how to use the case-cohort data to estimate the regression parameter of the additive hazards model, which specifies that the conditional hazard function given a set of covariates is the sum of an arbitrary baseline hazard function and a regression function of the covariates. The proposed estimator is shown to be consistent and asymptotically normal with an easily estimated variance. The subcohort may be selected by independent Bernoulli sampling with arbitrary selection probabilities or by stratified simple random sampling. The efficiencies of various sampling schemes are investigated both analytically and by simulation. A real example is provided.