Maximum likelihood estimation for Cox's regression model under nested case.control sampling

Nested case.control sampling is designed to reduce the costs of large cohort studies.It is important to estimate the parameters of interest as efficiently as possible.We present a new maximum likelihood estimator (MLE) for nested case.control sampling in the context of Cox's proportional hazards model.The MLE is computed by the EM.algorithm, which is easy to implement in the proportional hazards setting.Standard errors are estimated by a numerical profile likelihood approach based on EM aided differentiation.The work was motivated by a nested case.control study that hypothesized that insulin.like growth factor I was associated with ischemic heart disease.The study was based on a population of 3784 Danes and 231 cases of ischemic heart disease where controls were matched on age and gender.We illustrate the use of the MLE for these data and show how the maximum likelihood framework can be used to obtain information additional to the relative risk estimates of covariates.