Aslanyan Vahan, Pickering Trevor, Nuño Michelle, Renfro Lindsay A, Pa Judy, Mack Wendy J
Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, California, USA.
Children's Oncology Group, Monrovia, California, USA.
Pharm Stat. 2025 Mar-Apr;24(2):e2443. doi: 10.1002/pst.2443. Epub 2024 Oct 24.
Study designs incorporate interim analyses to allow for modifications to the trial design. These analyses may aid decisions regarding sample size, futility, and safety. Furthermore, they may provide evidence about potential differences between treatment arms. Bayesian response adaptive randomization (RAR) skews allocation proportions such that fewer participants are assigned to the inferior treatments. However, these allocation changes may introduce covariate imbalances. We discuss two versions of Bayesian RAR (with and without covariate adjustment for a binary covariate) for continuous outcomes analyzed using change scores and repeated measures, while considering either regression or mixed models for interim analysis modeling. Through simulation studies, we show that RAR (both versions) allocates more participants to better treatments compared to equal randomization, while reducing potential covariate imbalances. We also show that dynamic allocation using mixed models for repeated measures yields a smaller allocation proportion variance while having a similar covariate imbalance as regression models. Additionally, covariate imbalance was smallest for methods using covariate-adjusted RAR (CARA) in scenarios with small sample sizes and covariate prevalence less than 0.3. Covariate imbalance did not differ between RAR and CARA in simulations with larger sample sizes and higher covariate prevalence. We thus recommend a CARA approach for small pilot/exploratory studies for the identification of candidate treatments for further confirmatory studies.
研究设计纳入中期分析，以便对试验设计进行修改。这些分析可能有助于做出关于样本量、无效性和安全性的决策。此外，它们可能提供有关治疗组之间潜在差异的证据。贝叶斯响应自适应随机化（RAR）会使分配比例产生偏差，从而将较少的参与者分配到较差的治疗组。然而，这些分配变化可能会导致协变量失衡。我们讨论了两种版本的贝叶斯RAR（一种对二元协变量进行协变量调整，另一种不进行调整），用于使用变化分数和重复测量分析的连续结果，同时考虑在中期分析建模中使用回归模型或混合模型。通过模拟研究，我们表明，与等概率随机化相比，RAR（两种版本）会将更多参与者分配到更好地治疗组，同时减少潜在的协变量失衡。我们还表明，使用混合模型进行重复测量的动态分配产生的分配比例方差较小，同时具有与回归模型相似的协变量失衡。此外，在样本量较小且协变量患病率小于0.3的情况下，使用协变量调整RAR（CARA）的方法的协变量失衡最小。在样本量较大且协变量患病率较高的模拟中，RAR和CARA之间的协变量失衡没有差异。因此，我们建议在小型试点/探索性研究中采用CARA方法，以识别用于进一步验证性研究的候选治疗方法。
