IAME, UMR 1137, INSERM, University Paris Diderot, Paris, France.
Roche Pharma Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Basel, Switzerland.
Stat Med. 2021 May 10;40(10):2435-2451. doi: 10.1002/sim.8913. Epub 2021 Mar 2.
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.
在 II 期剂量探索试验具有挑战性的背景下，与治疗结束时的分析相比，纵向分析可能会增加药物效应检测的能力。这项工作提出了 cLRT-Mod，这是 MCP-Mod 方法的药物计量学改编，它允许使用非线性混合效应模型来首先检测剂量反应信号，然后在考虑模型结构不确定性的情况下确定确证阶段的剂量。该方法通过使用不同的方案对一个假设的 II 期剂量探索试验进行了广泛的临床试验模拟，并比较了不同的方法，如 MCP-Mod，对其进行了评估。结果表明，与 EOT 多重对照检验相比，在小样本量和弱药物效应的情况下，使用纵向数据的 cLRT 可提高功效，同时在数据分析之前保持模型的先验可预测性和名义的第一类错误率。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率，并避免置信区间大小的低估。最后，为了说明目的，将 cLRT-Mod 应用于真实的 II 期剂量探索试验的分析。
