Machine Learning
Bump hunting in high-dimensional data
Statistics and Computing
Computational Statistics & Data Analysis
Flexible patient rule induction method for optimizing process variables in discrete type
Expert Systems with Applications: An International Journal
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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.