Rybin Denis, Doros Gheorghe, Pencina Michael J, Fava Maurizio
Department of Biostatistics, Boston University, 801 Massachusetts Avenue, Boston, 02118, MA, U.S.A.
Harvard Clinical Research Institute (HCRI), 930 Commonwealth Avenue, 3rd floor, Boston, 02215, MA, U.S.A.
Stat Med. 2015 Jul 10;34(15):2281-93. doi: 10.1002/sim.6494. Epub 2015 Apr 10.
The Sequential Parallel Comparison Design (SPCD) is one of the novel approaches addressing placebo response. The analysis of SPCD data typically classifies subjects as 'placebo responders' or 'placebo non-responders'. Most current methods employed for analysis of SPCD data utilize only a part of the data collected during the trial. A repeated measures model was proposed for analysis of continuous outcomes that permitted the inclusion of information from all subjects into the treatment effect estimation. We describe here a new approach using a weighted repeated measures model that further improves the utilization of data collected during the trial, allowing the incorporation of information that is relevant to the placebo response, and dealing with the problem of possible misclassification of subjects. Our simulations show that when compared to the unweighted repeated measures model method, our approach performs as well or, under certain conditions, better, in preserving the type I error, achieving adequate power and minimizing the mean squared error.
序贯平行比较设计(SPCD)是解决安慰剂反应的新方法之一。SPCD数据的分析通常将受试者分为“安慰剂反应者”或“安慰剂无反应者”。目前用于分析SPCD数据的大多数方法仅利用试验期间收集的数据的一部分。有人提出了一种重复测量模型来分析连续结果,该模型允许将所有受试者的信息纳入治疗效果估计中。我们在此描述一种使用加权重复测量模型的新方法,该方法进一步提高了试验期间收集的数据的利用率,允许纳入与安慰剂反应相关的信息,并处理受试者可能错误分类的问题。我们的模拟表明,与未加权重复测量模型方法相比,我们的方法在保持I型错误、获得足够的检验效能和最小化均方误差方面表现相同,或在某些条件下表现更好。