Biostatistics & Health Data Science Unit, Institut Claudius Regaud - IUCT-O, 1 avenue Irène Joliot-Curie, 31059 Cedex 9, Toulouse, France.
Toulouse School of Economics, University of Toulouse Capitole, Toulouse, France.
BMC Med Res Methodol. 2022 Oct 26;22(1):278. doi: 10.1186/s12874-022-01748-w.
Given the inherent challenges of conducting randomized phase III trials in older cancer patients, single-arm phase II trials which assess the feasibility of a treatment that has already been shown to be effective in a younger population may provide a compelling alternative. Such an approach would need to evaluate treatment feasibility based on a composite endpoint that combines multiple clinical dimensions and to stratify older patients as fit or frail to account for the heterogeneity of the study population to recommend an appropriate treatment approach. In this context, stratified adaptive two-stage designs for binary or composite endpoints, initially developed for biomarker studies, allow to include two subgroups whilst maintaining competitive statistical performances. In practice, heterogeneity may indeed affect more than one dimension and incorporating co-primary endpoints, which independently assess each individual clinical dimension, would therefore appear quite pertinent. The current paper presents a novel phase II design for co-primary endpoints which takes into account the heterogeneity of a population. METHODS: We developed a stratified adaptive Bryant & Day design based on the Jones et al. and Parashar et al. algorithm. This two-stage design allows to jointly assess two dimensions (e.g. activity and toxicity) in two different subgroups. The operating characteristics of this new design were evaluated using examples and simulation comparisons with the Bryant & Day design in the context where the study population is stratified according to a pre-defined criterion.
Simulation results demonstrated that the new design minimized the expected and maximum sample sizes as compared to parallel Bryant & Day designs (one in each subgroup), whilst controlling type I error rates and maintaining a competitive statistical power as well as a high probability of detecting heterogeneity.
In a heterogeneous population, this two-stage stratified adaptive phase II design provides a useful alternative to classical one and allows to identify a subgroup of interest without dramatically increasing sample size. As heterogeneity is not limited to older populations, this new design may also be relevant to other study populations such as children or adolescents and young adults or the development of targeted therapies based on a biomarker.
鉴于在老年癌症患者中进行随机 III 期试验所固有的挑战，评估已经在年轻人群中显示有效的治疗方法在可行性的单臂 II 期试验可能是一个引人注目的替代方案。这种方法需要基于包含多个临床维度的复合终点来评估治疗可行性，并对老年患者进行适合或虚弱的分层，以考虑研究人群的异质性，从而推荐适当的治疗方法。在这种情况下，最初为生物标志物研究开发的用于二项或复合终点的分层自适应两阶段设计可以在保持竞争性统计性能的同时纳入两个亚组。实际上，异质性可能会影响多个维度，因此纳入独立评估每个临床维度的主要次要终点似乎非常相关。本文提出了一种新的用于主要次要终点的 II 期设计，该设计考虑了人群的异质性。方法：我们基于 Jones 等人和 Parashar 等人的算法开发了一种分层自适应 Bryant & Day 设计。这种两阶段设计允许在两个不同的亚组中联合评估两个维度（例如活性和毒性）。在根据预定义标准对研究人群进行分层的情况下，使用示例和与 Bryant & Day 设计的模拟比较评估了这种新设计的操作特征。结果：模拟结果表明，与在每个亚组中都有一个的平行 Bryant & Day 设计相比，新设计最小化了预期和最大样本量，同时控制了 I 型错误率并保持了有竞争力的统计功效以及高检测到异质性的可能性。结论：在异质人群中，这种两阶段分层自适应 II 期设计为经典设计提供了一种有用的替代方案，并可以识别出一个感兴趣的亚组，而不会大幅增加样本量。由于异质性不仅限于老年人群，因此这种新设计也可能适用于其他研究人群，例如儿童或青少年和年轻成年人，或基于生物标志物的靶向治疗的开发。
