The statistical methodology for model‐based dose finding under model uncertainty has attracted increasing attention in recent years. While the underlying principles are simple and easy to understand, developing and implementing an efficient approach for binary responses can be a formidable task in practice. Motivated by the statistical challenges encountered in a phase II dose finding study, we explore several key design and analysis issues related to the hybrid testing‐modeling approaches for binary responses. The issues include candidate model selection and specifications, optimal design and efficient sample size allocations, and, notably, the methods for dose‐response testing and estimation. Specifically, we consider a class of generalized linear models suited for the candidate set and establish D‐optimal designs for these models. Additionally, we propose using permutation‐based tests for dose‐response testing to avoid asymptotic normality assumptions typically required for contrast‐based tests. We perform trial simulations to enhance our understanding of these issues.

中文翻译：

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模型不确定性下二元响应基于模型的剂量查找中的统计考虑

近年来，模型不确定性下基于模型的剂量发现的统计方法引起了越来越多的关注。虽然基本原理简单易懂，但开发和实施有效的二元响应方法在实践中可能是一项艰巨的任务。受到 II 期剂量探索研究中遇到的统计挑战的启发，我们探索了与二元反应的混合测试建模方法相关的几个关键设计和分析问题。这些问题包括候选模型的选择和规格、优化设计和有效的样本量分配，尤其是剂量反应测试和估计的方法。具体来说，我们考虑一类适合候选集的广义线性模型，并为这些模型建立 D 最优设计。此外，我们建议使用基于排列的测试进行剂量反应测试，以避免基于对比的测试通常需要的渐近正态性假设。我们进行试验模拟以增强我们对这些问题的理解。