James Robins

**Research** The principal focus of Dr. Robins' research has been the development of analytic methods appropriate for drawing causal inferences from complex observational and randomized studies with time-varying exposures or treatments. The new methods are to a large extent based on the estimation of the parameters of a new class of causal models - the structural nested models - using a new class of estimators - the G estimators. The usual approach to the estimation of the effect of a time-varying treatment or exposure on time to disease is to model the hazard incidence of failure at time t as a function of past treatment history using a time-dependent Cox proportional hazards model. Dr. Robins has shown the usual approach may be biased whether or not further adjusts for past confounder history in the analysis when: (A1) there exists a time-dependent risk factor for or predictor of the event of interest that also predicts subsequent treatment, and (A2) past treatment history pred