Identifying Homogeneous Subgroups in Neurological Disorders: Unbiased Recursive Partitioning in Cervical Complete Spinal Cord Injury.
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.
Spinal Cord Injury Centre, Balgrist University Hospital, Zurich, Switzerland Division of Biostatistics, Institute for Social and Preventive Medicine, University of Zurich, Zurich, Switzerland ltanadini@paralab.balgrist.ch.
Lorenzo G
LG
Tanadini
ICORD, University of British Columbia and Vancouver Coastal Health, Vancouver, British Columbia, Canada.
John D
JD
Steeves
Division of Biostatistics, Institute for Social and Preventive Medicine, University of Zurich, Zurich, Switzerland.
Torsten
T
Hothorn
Trauma Center Bayreuth, Bayreuth, Germany EMSCI Study Group.
Rainer
R
Abel
Trauma Center Murnau, Murnau, Germany EMSCI Study Group.
Doris
D
Maier
Spinal Cord Injury Centre, Balgrist University Hospital, Zurich, Switzerland EMSCI Study Group.
Martin
M
Schubert
Spinal Cord Unjury Center, Heidelberg University Hospital, Heidelberg, Germany EMSCI Study Group.
Norbert
N
Weidner
Spinal Cord Unjury Center, Heidelberg University Hospital, Heidelberg, Germany EMSCI Study Group.
Rüdiger
R
Rupp
Spinal Cord Injury Centre, Balgrist University Hospital, Zurich, Switzerland EMSCI Study Group.
Armin
A
Curt
D066193:Cervical Cord / Q000293:injuries*; D003627:Data Interpretation, Statistical; D005060:Europe; D006801:Humans; D008137:Longitudinal Studies; D017063:Outcome Assessment, Health Care / Q000379:methods*; D011379:Prognosis; D012189:Retrospective Studies; D012720:Severity of Illness Index; D013119:Spinal Cord Injuries / Q000175:diagnosis* / Q000503:physiopathology*; D034941:Upper Extremity / Q000503:physiopathology*
cervical; clinical trial; motor level; outcome prediction; sensorimotor complete; upper extremity motor score