Exploratory identification of predictive biomarkers in randomized trials with normal endpoints

One of the main endeavours in present-day medicine, especially in oncological research, is to provide evidence for individual treatment decisions ("stratified medicine"). In the pursuit of optimal treatment decision rules, the identification of predictive biomarkers that modify the treatment effect is essential. Proposed methods have often been based on recursive partitioning since a wide variety of interaction patterns can be captured automatically and the results are easily interpretable. Furthermore, these methods are readily extendable to high-dimensional settings by means of ensemble learning. In this article, we present predMOB, an adaptation of the model-based recursive partitioning (MOB) for subgroup analysis approach specifically tailored to the identification of predictive factors. In a simulation study, predMOB outperforms the original MOB with respect to the number of false detections and shows to be more robust in moderately complex settings. Furthermore, we compare the results of predMOB for the application to a public data base of amyotrophic lateral sclerosis patients to those obtained from the original MOB and are able to elucidate the nature of the biomarkers' effects.