Dose selection is critical in pharmaceutical drug development, as it directly impacts therapeutic efficacy and patient safety of a drug. The Generalized Multiple Comparison Procedures and Modeling (MCP-Mod) 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.

翻译：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性。广义多重比较程序与建模（MCP-Mod）方法通常用于II期临床试验中，以检验和估计剂量-反应关系。然而，在小样本情况下，尤其是针对二元终点时，其有效性受到诸如逻辑回归中完全分离等问题的限制，导致估计值不存在。受一项实际采用MCP-Mod方法的临床试验启发，本文引入惩罚最大似然估计（MLE）和基于随机化的推断技术以应对这些挑战。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，基于随机化的检验能够在小到中等样本量中提高统计功效，同时即使在存在时间趋势的情况下也能控制I类错误率。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理学计量学场景，证明了其在此类情境中的有效性。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，尤其是在小样本背景下。