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Identifying homogeneous subgroups in neurological disorders: unbiased recursive partitioning in cervical complete spinal cord injury


Tanadini, Lorenzo G; Steeves, John D; Hothorn, Torsten; Abel, Rainer; Maier, Doris; Schubert, Martin; Weidner, Norbert; Rupp, Rüdiger; Curt, Armin (2014). Identifying homogeneous subgroups in neurological disorders: unbiased recursive partitioning in cervical complete spinal cord injury. Neurorehabilitation and Neural Repair, 28(6):507-515.

Abstract

BACKGROUND: The reliable stratification of homogeneous subgroups and the prediction of future clinical outcomes within heterogeneous neurological disorders is a particularly challenging task. Nonetheless, it is essential for the implementation of targeted care and effective therapeutic interventions.
OBJECTIVE: This study was designed to assess the value of a recently developed regression tool from the family of unbiased recursive partitioning methods in comparison to established statistical approaches (eg, linear and logistic regression) for predicting clinical endpoints and for prospective patients' stratification for clinical trials.
METHODS: A retrospective, longitudinal analysis of prospectively collected neurological data from the European Multicenter study about Spinal Cord Injury (EMSCI) network was undertaken on C4-C6 cervical sensorimotor complete subjects. Predictors were based on a broad set of early (<2 weeks) clinical assessments. Endpoints were based on later clinical examinations of upper extremity motor scores and recovery of motor levels, at 6 and 12 months, respectively. Prediction accuracy for each statistical analysis was quantified by resampling techniques.
RESULTS: For all settings, overlapping confidence intervals indicated similar prediction accuracy of unbiased recursive partitioning to established statistical approaches. In addition, unbiased recursive partitioning provided a direct way of identification of more homogeneous subgroups. The partitioning is carried out in a data-driven manner, independently from a priori decisions or predefined thresholds.
CONCLUSION: Unbiased recursive partitioning techniques may improve prediction of future clinical endpoints and the planning of future SCI clinical trials by providing easily implementable, data-driven rationales for early patient stratification based on simple decision rules and clinical read-outs.

BACKGROUND: The reliable stratification of homogeneous subgroups and the prediction of future clinical outcomes within heterogeneous neurological disorders is a particularly challenging task. Nonetheless, it is essential for the implementation of targeted care and effective therapeutic interventions.
OBJECTIVE: This study was designed to assess the value of a recently developed regression tool from the family of unbiased recursive partitioning methods in comparison to established statistical approaches (eg, linear and logistic regression) for predicting clinical endpoints and for prospective patients' stratification for clinical trials.
METHODS: A retrospective, longitudinal analysis of prospectively collected neurological data from the European Multicenter study about Spinal Cord Injury (EMSCI) network was undertaken on C4-C6 cervical sensorimotor complete subjects. Predictors were based on a broad set of early (<2 weeks) clinical assessments. Endpoints were based on later clinical examinations of upper extremity motor scores and recovery of motor levels, at 6 and 12 months, respectively. Prediction accuracy for each statistical analysis was quantified by resampling techniques.
RESULTS: For all settings, overlapping confidence intervals indicated similar prediction accuracy of unbiased recursive partitioning to established statistical approaches. In addition, unbiased recursive partitioning provided a direct way of identification of more homogeneous subgroups. The partitioning is carried out in a data-driven manner, independently from a priori decisions or predefined thresholds.
CONCLUSION: Unbiased recursive partitioning techniques may improve prediction of future clinical endpoints and the planning of future SCI clinical trials by providing easily implementable, data-driven rationales for early patient stratification based on simple decision rules and clinical read-outs.

Citations

9 citations in Web of Science®
10 citations in Scopus®
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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > Balgrist University Hospital, Swiss Spinal Cord Injury Center
04 Faculty of Medicine > Epidemiology, Biostatistics and Prevention Institute (EBPI)
Dewey Decimal Classification:610 Medicine & health
Language:English
Date:2014
Deposited On:11 Apr 2014 14:27
Last Modified:05 Apr 2016 17:49
Publisher:SAGE Publications
ISSN:1545-9683
Publisher DOI:https://doi.org/10.1177/1545968313520413
PubMed ID:24477680

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