Tanadini Lorenzo G, Steeves John D, Hothorn Torsten, Abel Rainer, Maier Doris, Schubert Martin, Weidner Norbert, Rupp Rüdiger, Curt Armin
Spinal Cord Injury Centre, Balgrist University Hospital, Zurich, Switzerland Division of Biostatistics, Institute for Social and Preventive Medicine, University of Zurich, Zurich, Switzerland
ICORD, University of British Columbia and Vancouver Coastal Health, Vancouver, British Columbia, Canada.
Neurorehabil Neural Repair. 2014 Jul;28(6):507-15. doi: 10.1177/1545968313520413. Epub 2014 Jan 28.
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.
背景 在异质性神经疾病中对同质亚组进行可靠分层并预测未来临床结果是一项特别具有挑战性的任务。尽管如此，这对于实施针对性护理和有效的治疗干预至关重要。目的 本研究旨在评估一种最近开发的、来自无偏递归划分方法家族的回归工具与既定统计方法（如线性和逻辑回归）相比，在预测临床终点以及对临床试验中的前瞻性患者进行分层方面的价值。方法 对来自欧洲多中心脊髓损伤研究（EMSCI）网络的前瞻性收集的神经学数据进行回顾性纵向分析，研究对象为C4 - C6节段颈髓感觉运动完全损伤的患者。预测指标基于一系列广泛的早期（<2周）临床评估。终点分别基于6个月和12个月时上肢运动评分和运动水平恢复的后期临床检查。通过重采样技术量化每种统计分析的预测准确性。结果 在所有情况下，重叠的置信区间表明无偏递归划分与既定统计方法具有相似的预测准确性。此外，无偏递归划分提供了一种直接识别更同质亚组的方法。划分是以数据驱动的方式进行的，独立于先验决策或预定义阈值。结论 无偏递归划分技术可能通过基于简单决策规则和临床指标为早期患者分层提供易于实施的数据驱动原理，从而改善对未来临床终点的预测以及未来脊髓损伤临床试验的规划。
