Data Science Institute (DSI), Interuniversity Institute for Biostatistics and statistical Bioinformatics (I-BioStat), Hasselt University, Hasselt, Belgium.
Team Biostatistics and Big Medical Data, Intelligent Data Analytics (IDA) Lab Salzburg, Paracelsus Medical University, Salzburg, Austria.
Biometrics. 2023 Dec;79(4):3998-4011. doi: 10.1111/biom.13920. Epub 2023 Aug 16.
To optimize the use of data from a small number of subjects in rare disease trials, an at first sight advantageous design is the repeated measures cross-over design. However, it is unclear how these within-treatment period and within-subject clustered data are best analyzed in small-sample trials. In a real-data simulation study based upon a recent epidermolysis bullosa simplex trial using this design, we compare non-parametric marginal models, generalized pairwise comparison models, GEE-type models and parametric model averaging for both repeated binary and count data. The recommendation of which methodology to use in rare disease trials with a repeated measures cross-over design depends on the type of outcome and the number of time points the treatment has an effect on. The non-parametric marginal model testing the treatment-time-interaction effect is suitable for detecting between group differences in the shapes of the longitudinal profiles. For binary outcomes with the treatment effect on a single time point, the parametric model averaging method is recommended, while in the other cases the unmatched generalized pairwise comparison methodology is recommended. Both provide an easily interpretable effect size measure, and do not require exclusion of periods or subjects due to incompleteness.
为了优化在罕见病试验中利用少量受试者的数据，一种乍看起来有利的设计是重复测量交叉设计。然而，在小样本试验中，如何最好地分析这些治疗内期间和个体内聚类数据尚不清楚。在一项基于最近使用这种设计的单纯性大疱性表皮松解症试验的真实数据模拟研究中，我们比较了重复二分类和计数数据的非参数边际模型、广义成对比较模型、GEE 型模型和参数模型平均。在具有重复测量交叉设计的罕见病试验中，推荐使用哪种方法取决于结局类型和治疗对时间点的影响数量。测试治疗-时间交互效应的非参数边际模型适用于检测纵向曲线形状的组间差异。对于在单个时间点上具有治疗效果的二分类结局，推荐使用参数模型平均方法，而在其他情况下，推荐使用不匹配的广义成对比较方法。这两种方法都提供了一个易于解释的效应量度量，并且不需要由于不完整而排除时期或个体。
