论文标题
天然立方花纹用于分析阿尔茨海默氏症的临床试验
Natural cubic splines for the analysis of Alzheimer's clinical trials
论文作者
论文摘要
混合模型重复测量（MMRM）是阿尔茨海默氏病的临床试验中使用的最常见的分析方法和其他随着时间的评估，这些疾病和其他进行的进行性疾病测量。该模型将时间视为一个分类变量，该变量允许对每个随机组中每个研究访问的平均值进行无约束的估计。当评估发生在分类外时，以这种方式对时间进行分类可能会出现问题，因为包括安排外访问可能会引起偏见，并排除它们忽略了有价值的信息并违反了处理原则的意图。由于临床试验访问，由于COVID19大流行而被延迟，这一问题加剧了。作为MMRM的替代方案，我们提出了一种有限的纵向数据分析，其天然立方花纹将时间视为连续的时间，并使用测试版本效应来对平均时间进行建模。在实践和仿真研究中，样条模型与MMRM和具有比例治疗效果的模型等分类时间模型相比被证明是优越的。
Mixed model repeated measures (MMRM) is the most common analysis approach used in clinical trials for Alzheimer's disease and other progressive diseases measured with continuous outcomes measured over time. The model treats time as a categorical variable, which allows an unconstrained estimate of the mean for each study visit in each randomized group. Categorizing time in this way can be problematic when assessments occur off-schedule, as including off-schedule visits can induce bias, and excluding them ignores valuable information and violates the intention to treat principle. This problem has been exacerbated by clinical trial visits which have been delayed due to the COVID19 pandemic. As an alternative to MMRM, we propose a constrained longitudinal data analysis with natural cubic splines that treats time as continuous and uses test version effects to model the mean over time. The spline model is shown to be superior, in practice and simulation studies, to categorical-time models like MMRM and models that assume a proportional treatment effect.
