Institute of Neuroscience, Newcastle University, Sir James Spence Institute, Royal Victoria Infirmary, Newcastle upon Tyne, United Kingdom.
Neurorehabil Neural Repair. 2010 Mar-Apr;24(3):225-34. doi: 10.1177/1545968309354534. Epub 2009 Dec 3.
The identification of possible treatment effects against a background of spontaneous recovery is a major challenge to the successful completion of randomized clinical trials (RCTs) in rehabilitation research. Conventional trial outcomes such as the differences between group means of an outcome measure at a fixed time point are inefficient to an extent that is a major problem, particularly for exploratory studies seeking preliminary evidence of efficacy.
To quantitate gains in study power over conventional fixed-end-point designs by using parametric end points derived from the modeling of the time course of recovery after brain injury.
Nonlinear mixed effects (NLME) modeling of the recovery trajectories of 103 children rehabilitating after traumatic brain injury (TBI) as reflected in serial WeeFIM scores was performed. Pseudoreplicate data sets were generated replicating the statistical characteristics of the original data set, and these formed the basis of clinical trial simulations to derive robust estimates of study power.
Parametric end points derived from modeling of recovery improve study power (and reduce necessary sample size) by up to 5 times in this example.
Parametric end points derived from models of recovery trajectories offer an efficient alternative design for exploratory clinical studies of rehabilitation interventions.
在自发恢复的背景下识别可能的治疗效果，是成功完成康复研究中随机临床试验（RCT）的主要挑战。常规试验结局，如在固定时间点的结局测量组间均值差异，在某种程度上效率低下，这是一个主要问题，特别是对于探索性研究寻求疗效的初步证据。
通过使用源自脑损伤后恢复过程建模的参数终点，量化研究能力相对于传统固定终点设计的提高。
对 103 名创伤性脑损伤（TBI）后康复的儿童的 WeeFIM 评分进行了非线性混合效应（NLME）建模，反映了恢复轨迹。生成了复制原始数据集统计特征的伪重复数据集，并以此为基础进行临床试验模拟，得出研究能力的稳健估计。
在本例中，从恢复建模中得出的参数终点可将研究能力提高（并减少所需的样本量）多达 5 倍。
从恢复轨迹模型中得出的参数终点为康复干预的探索性临床研究提供了一种有效的替代设计。
