Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany.
DZHK (German Center for Cardiovascular Research), partner site Göttingen, Göttingen, Germany.
Biom J. 2023 Feb;65(2):e2000345. doi: 10.1002/bimj.202000345. Epub 2022 Aug 19.
In the precision medicine era, (prespecified) subgroup analyses are an integral part of clinical trials. Incorporating multiple populations and hypotheses in the design and analysis plan, adaptive designs promise flexibility and efficiency in such trials. Adaptations include (unblinded) interim analyses (IAs) or blinded sample size reviews. An IA offers the possibility to select promising subgroups and reallocate sample size in further stages. Trials with these features are known as adaptive enrichment designs. Such complex designs comprise many nuisance parameters, such as prevalences of the subgroups and variances of the outcomes in the subgroups. Additionally, a number of design options including the timepoint of the sample size review and timepoint of the IA have to be selected. Here, for normally distributed endpoints, we propose a strategy combining blinded sample size recalculation and adaptive enrichment at an IA, that is, at an early timepoint nuisance parameters are reestimated and the sample size is adjusted while subgroup selection and enrichment is performed later. We discuss implications of different scenarios concerning the variances as well as the timepoints of blinded review and IA and investigate the design characteristics in simulations. The proposed method maintains the desired power if planning assumptions were inaccurate and reduces the sample size and variability of the final sample size when an enrichment is performed. Having two separate timepoints for blinded sample size review and IA improves the timing of the latter and increases the probability to correctly enrich a subgroup.
在精准医学时代，（预设）亚组分析是临床试验的一个组成部分。在设计和分析计划中纳入多个群体和假设，适应性设计承诺在这些试验中具有灵活性和效率。适应性设计包括（非盲）中期分析（IA）或盲样本量审查。IA 提供了选择有前途的亚组并在进一步阶段重新分配样本量的可能性。具有这些特征的试验被称为适应性富集设计。这种复杂的设计包括许多干扰参数，例如亚组的患病率和亚组中结果的方差。此外，还必须选择许多设计选项，包括样本量审查的时间点和 IA 的时间点。在这里，对于正态分布的终点，我们提出了一种结合盲法样本量重算和 IA 适应性富集的策略，即在早期，重新估计干扰参数，并调整样本量，同时进行亚组选择和富集。我们讨论了不同方差情景以及盲法审查和 IA 时间点的影响，并在模拟中研究了设计特征。如果规划假设不准确，该方法可保持所需的功效，并在进行富集时减少样本量和最终样本量的变异性。对于盲法样本量审查和 IA 有两个单独的时间点，可以改善后者的时间安排，并增加正确富集亚组的可能性。
