Accurate dose selection in Phase II trials is critical to the success of subsequent Phase III trials, but suboptimal choices remain a leading cause of trial failure and regulatory rejection. Although MCP-Mod is widely adopted and endorsed by regulatory agencies, it requires prespecification of candidate models and is highly sensitive to model misspecification. To address these challenges, we introduce MAP-curvature, a general model-free framework for dose-response modelling that penalises the total curvature of the dose-response curve through a prior. Within this framework, LiMAP-curvature arises as the linear special case, whereas SEMAP-curvature, the focus of this work, employs the sigmoid Emax model, providing greater flexibility to capture nonlinear pharmacological patterns. Through extensive simulations, we show that SEMAP-curvature generally outperforms LiMAP-curvature and MCP-Mod in detecting the dose-response signal, estimating the dose-response curve and identifying the minimum effective dose, with particularly significant improvements under concave downward shapes resembling the sigmoid Emax model. Although SEMAP-curvature exhibits slightly greater variability, it remains robust in accuracy and reliability. We further extend MAP-curvature by integrating it with the Bayesian hierarchical model to enable flexible borrowing of historical data, which improves power and precision, particularly when dose levels overlap across studies. These results highlight MAP-curvature, and in particular SEMAP-curvature with historical borrowing, as a robust and efficient framework for dose selection in early-phase clinical trials.

翻译：在II期试验中准确选择剂量对于后续III期试验的成功至关重要，但次优选择仍是试验失败和监管机构拒绝的主要原因。尽管MCP-Mod被监管机构广泛采纳和认可，但它需要预先设定候选模型，并且对模型设定错误高度敏感。为应对这些挑战，我们提出了MAP-curvature，这是一种通用的无模型剂量反应建模框架，通过先验惩罚剂量反应曲线的总曲率。在此框架中，LiMAP-curvature作为线性特例出现，而本文重点研究的SEMAP-curvature则采用Sigmoid Emax模型，为捕捉非线性药理学模式提供了更大的灵活性。通过大量模拟，我们证明SEMAP-curvature在检测剂量反应信号、估计剂量反应曲线和识别最小有效剂量方面通常优于LiMAP-curvature和MCP-Mod，在类似Sigmoid Emax模型的下凹形状下改进尤为显著。尽管SEMAP-curvature表现出稍大的变异性，但其准确性和可靠性依然稳健。我们进一步将MAP-curvature与贝叶斯分层模型相结合，实现了历史数据的灵活借阅，从而提高了检验效能和精确度，尤其在研究间剂量水平重叠时效果更佳。这些结果突显了MAP-curvature，特别是结合历史借阅的SEMAP-curvature，作为早期临床试验中稳健高效的剂量选择框架的价值。