Within the challenging context of phase II dose‐finding trials, longitudinal analyses may increase drug effect detection power compared to an end‐of‐treatment analysis. This work proposes cLRT‐Mod, a pharmacometric adaptation of the MCP‐Mod methodology, which allows the use of nonlinear mixed effect models to first detect a dose‐response signal and then identify the doses for the confirmatory phase while accounting for model structure uncertainty. The method was evaluated through extensive clinical trial simulations of a hypothetical phase II dose‐finding trial using different scenarios and comparing different methods such as MCP‐Mod. The results show an increase in power using cLRT with longitudinal data compared to an EOT multiple contrast tests for scenarios with small sample size and weak drug effect while maintaining pre‐specifiability of the models prior to data analysis and the nominal type I error. This work shows how model averaging provides better coverage probability of the drug effect in the prediction step, and avoids under‐estimation of the size of the confidence interval. Finally, for illustration purpose cLRT‐Mod was applied to the analysis of a real phase II dose‐finding trial.

中文翻译：

---

cLRT-Mod：一种在模型不确定性下基于药理学模型分析纵向 II 期剂量发现研究的有效方法

在 II 期剂量寻找试验的挑战性背景下，与治疗结束分析相比，纵向分析可能会提高药物效应检测能力。这项工作提出了 cLRT-Mod，它是 MCP-Mod 方法的药理学适应，它允许使用非线性混合效应模型首先检测剂量反应信号，然后确定确认阶段的剂量，同时考虑模型结构的不确定性。该方法通过对假设的 II 期剂量寻找试验的广泛临床试验模拟进行了评估，该试验使用不同的场景并比较了不同的方法，例如 MCP-Mod。结果表明，与 EOT 多重对比测试相比，在样本量小和药物作用较弱的情况下，使用带有纵向数据的 cLRT 的功效增加，同时在数据分析和名义 I 型错误之前保持模型的预先指定性。这项工作展示了模型平均如何在预测步骤中提供更好的药物效应覆盖概率，并避免低估置信区间的大小。最后，为了说明目的，将 cLRT-Mod 应用于实际 II 期剂量寻找试验的分析。