TY  - JOUR
AU  - Krzykalla, Julia
AU  - Benner, Axel
AU  - Kopp-Schneider, Annette
TI  - Exploratory identification of predictive biomarkers in randomized trials with normal endpoints.
JO  - Statistics in medicine
VL  - 39
IS  - 7
SN  - 0277-6715
CY  - Chichester [u.a.]
PB  - Wiley
M1  - DKFZ-2019-03261
SP  - 923-939
PY  - 2020
N1  - #EA:C060#LA:C060#2020 Mar 30;39(7):923-939
AB  - 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.
LB  - PUB:(DE-HGF)16
C6  - pmid:31863499
DO  - DOI:10.1002/sim.8452
UR  - https://inrepo02.dkfz.de/record/148748
ER  -