Alali Aziz S, Vavrek Darcy, Barber Jason, Dikmen Sureyya, Nathens Avery B, Temkin Nancy R
1 Institute of Health Policy, University of Toronto , Toronto, Ontario, Canada .
J Neurotrauma. 2015 Apr 15;32(8):581-9. doi: 10.1089/neu.2014.3495. Epub 2015 Feb 9.
Batteries of functional and cognitive measures have been proposed as alternatives to the Extended Glasgow Outcome Scale (GOSE) as the primary outcome for traumatic brain injury (TBI) trials. We evaluated several approaches to analyzing GOSE and a battery of four functional and cognitive measures. Using data from a randomized trial, we created a "super" dataset of 16,550 subjects from patients with complete data (n=331) and then simulated multiple treatment effects across multiple outcome measures. Patients were sampled with replacement (bootstrapping) to generate 10,000 samples for each treatment effect (n=400 patients/group). The percentage of samples where the null hypothesis was rejected estimates the power. All analytic techniques had appropriate rates of type I error (≤5%). Accounting for baseline prognosis either by using sliding dichotomy for GOSE or using regression-based methods substantially increased the power over the corresponding analysis without accounting for prognosis. Analyzing GOSE using multivariate proportional odds regression or analyzing the four-outcome battery with regression-based adjustments had the highest power, assuming equal treatment effect across all components. Analyzing GOSE using a fixed dichotomy provided the lowest power for both unadjusted and regression-adjusted analyses. We assumed an equal treatment effect for all measures. This may not be true in an actual clinical trial. Accounting for baseline prognosis is critical to attaining high power in Phase III TBI trials. The choice of primary outcome for future trials should be guided by power, the domain of brain function that an intervention is likely to impact, and the feasibility of collecting outcome data.
功能和认知测量组合已被提议作为扩展格拉斯哥预后量表(GOSE)的替代方案,作为创伤性脑损伤(TBI)试验的主要结局指标。我们评估了几种分析GOSE以及一组四项功能和认知测量指标的方法。利用一项随机试验的数据,我们从具有完整数据的患者(n = 331)中创建了一个包含16550名受试者的“超级”数据集,然后在多个结局指标上模拟多种治疗效果。对患者进行有放回抽样(自助法),为每种治疗效果生成10000个样本(每组n = 400名患者)。零假设被拒绝的样本百分比估计了检验效能。所有分析技术的I型错误率均合适(≤5%)。通过对GOSE使用滑动二分法或使用基于回归的方法来考虑基线预后,相比于不考虑预后的相应分析,检验效能大幅提高。假设所有成分的治疗效果相同,使用多变量比例优势回归分析GOSE或使用基于回归的调整分析四项结局指标组合时检验效能最高。使用固定二分法分析GOSE在未调整分析和回归调整分析中检验效能最低。我们假设所有测量指标的治疗效果相同。在实际临床试验中可能并非如此。在III期TBI试验中,考虑基线预后对于获得高检验效能至关重要。未来试验主要结局指标的选择应以检验效能、干预可能影响的脑功能领域以及收集结局数据的可行性为指导。