Pin Lukas, Sverdlov Oleksandr, Bretz Frank, Bornkamp Björn
MRC Biostatistics Unit, University of Cambridge, Cambridge, UK.
Early Development Analytics, Novartis Pharmaceuticals Corporation, East Hanover, NJ, USA.
Stat Med. 2025 May;44(10-12):e70092. doi: 10.1002/sim.70092.
Dose selection is critical in pharmaceutical drug development, as it directly impacts therapeutic efficacy and patient's safety of a drug. The Generalized Multiple Comparison Procedures and Modeling approach is commonly used in Phase II trials for testing and estimation of dose-response relationships. However, its effectiveness in small sample sizes, particularly with binary endpoints, is hindered by issues like complete separation in logistic regression, leading to non existence of estimates. Motivated by an actual clinical trial using the MCP-Mod approach, this paper introduces penalized maximum likelihood estimation (MLE) and randomization-based inference techniques to address these challenges. Randomization-based inference allows for exact finite sample inference, while population-based inference for MCP-Mod typically relies on asymptotic approximations. Simulation studies demonstrate that randomization-based tests can enhance statistical power in small to medium-sized samples while maintaining control over type-I error rates, even in the presence of time trends. Our results show that residual-based randomization tests using penalized MLEs not only improve computational efficiency but also outperform standard randomization-based methods, making them an adequate choice for dose-finding analyses within the MCP-Mod framework. Additionally, we apply these methods to pharmacometric settings, demonstrating their effectiveness in such scenarios. The results in this paper underscore the potential of randomization-based inference for the analysis of dose-finding trials, particularly in small sample contexts.
剂量选择在药物研发中至关重要，因为它直接影响药物的治疗效果和患者安全。广义多重比较程序和建模方法常用于II期试验，以测试和估计剂量反应关系。然而，在小样本量情况下，尤其是对于二元终点，其有效性受到逻辑回归中完全分离等问题的阻碍，导致估计不存在。受一项使用MCP-Mod方法的实际临床试验启发，本文引入惩罚最大似然估计（MLE）和基于随机化的推断技术来应对这些挑战。基于随机化的推断允许进行精确的有限样本推断，而MCP-Mod的基于总体的推断通常依赖于渐近近似。模拟研究表明，基于随机化的检验可以提高中小样本的统计功效，同时在存在时间趋势的情况下，保持对I型错误率的控制。我们的结果表明，使用惩罚MLE的基于残差的随机化检验不仅提高了计算效率，而且优于标准的基于随机化的方法，使其成为MCP-Mod框架内剂量探索分析的合适选择。此外，我们将这些方法应用于药代动力学设置，证明了它们在此类情况下的有效性。本文的结果强调了基于随机化的推断在剂量探索试验分析中的潜力，特别是在小样本情况下。
