Responder identification in clinical trials with censored data
A new technique is presented for identification of positive and negative responders to a new treatment which was compared to a classical treatment (or placebo) in a randomized clinical trial with respect to survival time. This method was primarily developed for trials in which the two treatment arms do not differ in overall survival. It checks in a systematic manner if certain subgroups, described by so-called predictive factors, do show difference in survival due to the new treatment. The method relies on a good prognostic model built on one arm treated with placebo or the classical therapy. It employs the martingale residuals of the prognostic model in a stabilized bump hunting procedure, which finds groups of responders in the new treatment group with large positive or large negative residuals. The results of a simulation study are presented, comparing the performance of the new method to that of the Cox-PH model with treatment-covariate interactions. In a simulation study on average the proposed method recognizes in 90% the correct positive responder group and in 99% the correct negative responder group. The method is to be used in explorative analysis for hypothesis generation. The results are to be validated in future studies. (C) 2004 Elsevier B.V. All rights reserved.
responder identification predictive factors bump hunting treatment-covariate interaction