The proportional hazards model is frequently used in analyzing the results of clinical trials, when it is often the case that the outcomes are right-censored. This model allows one to measure treatment effects and simultaneously identify and adjust for prognostic factors that might influence the outcome. In this paper, we outline a class of semiparametric models that allows one to model prognostic factors nonlinearly, and have the data suggest the form of their effect. The methods are illustrated in an analysis of data from a breast cancer clinical trial.