J Neurotrauma. 2021 Feb 23. doi: 10.1089/neu.2020.7407. Online ahead of print.
ABSTRACT
Neurological disorders usually present very heterogeneous recovery patterns. Nonetheless, accurate prediction of future clinical endpoints and robust definition of homogeneous cohorts are necessary for scientific investigation and targeted care. For this, unbiased recursive partitioning with conditional inference trees (URP-CTREE) has received increasing attention in medical research, especially, but not limited to traumatic spinal cord injuries (SCI). URP-CTREE was introduced to SCI as a clinical guidance tool to explore and define homogeneous outcome groups by clinical means, while providing high accuracy in predicting future clinical outcomes. The validity and predictive value of URP-CTREE to provide improvements compared to other more common approaches applied by clinicians has recently come under critical scrutiny. Therefore, a comprehensive simulation study based on traumatic, cervical complete spinal cord injuries provides a framework to investigate and quantify the issues raised. Firstly, we assessed the replicability and robustness of URP-CTREE to identify homogeneous subgroups. Secondly, we implemented a prediction performance comparison of URP-CTREE with traditional statistical techniques, such as linear or logistic regression, and a novel machine learning method. URP-CTREE’s ability to identify homogeneous subgroups proved to be replicable and robust. In terms of prediction, URP-CTREE yielded a high prognostic performance comparable to a machine learning algorithm. The simulation study provides strong evidence for the robustness of URP-CTREE, which is achieved without compromising prediction accuracy. The slightly lower prediction performance is offset by URP-CTREE’s straightforward interpretation and application in clinical settings based on simple, data-driven decision rules.
PMID:33619988 | DOI:10.1089/neu.2020.7407
