论文标题
临床试验设计的敏感性分析：选择场景和总结操作特征
Sensitivity Analyses of Clinical Trial Designs: Selecting Scenarios and Summarizing Operating Characteristics
论文作者
论文摘要
基于模拟的灵敏度分析的使用是评估和比较未来临床试验的候选设计的基础。在这种情况下，灵敏度分析对于评估重要设计工作特性（OC）的依赖性特别有用。 OC的典型示例包括检测治疗效果的可能性和平均研究持续时间，这些持续时间取决于临床研究开始后才知道的UPS，例如主要结果和患者特征的分布。灵敏度分析的两个关键组成部分是（i）选择一组合理的模拟方案$ \ {\boldsymbolθ_1，...，\boldsymbolθ_k\} $和（ii）OCS列表。我们提出了一种新的方法，以选择一组方案，以纳入设计灵敏度分析。我们的方法平衡了在几种情况下计算出的对OC的简单性和解释性的需求，并需要通过模拟来忠实地总结OCS在UPS的所有合理价值中如何变化。我们的建议还支持选择将包含在最终灵敏度分析报告中的模拟场景数量的选择。要实现这些目标，我们最大程度地减少了损失函数$ \ MATHCAL {l}（\BoldSymbolθ_1，...，...，\BoldSymbolθ_k）$，该$是否形式化了一组特定的$ k $ sexitivity scenarios $ \ \ {\boldsymbolθ_1，... UPS的合理值。然后，我们使用优化技术选择最佳的仿真方案集来体现试验设计的OC。
The use of simulation-based sensitivity analyses is fundamental to evaluate and compare candidate designs for future clinical trials. In this context, sensitivity analyses are especially useful to assess the dependence of important design operating characteristics (OCs) with respect to various unknown parameters (UPs). Typical examples of OCs include the likelihood of detecting treatment effects and the average study duration, which depend on UPs that are not known until after the onset of the clinical study, such as the distributions of the primary outcomes and patient profiles. Two crucial components of sensitivity analyses are (i) the choice of a set of plausible simulation scenarios $\{\boldsymbolθ_1,...,\boldsymbolθ_K\}$ and (ii) the list of OCs of interest. We propose a new approach to choose the set of scenarios for inclusion in design sensitivity analyses. Our approach balances the need for simplicity and interpretability of OCs computed across several scenarios with the need to faithfully summarize -- through simulations -- how the OCs vary across all plausible values of the UPs. Our proposal also supports the selection of the number of simulation scenarios to be included in the final sensitivity analysis report. To achieve these goals, we minimize a loss function $\mathcal{L}(\boldsymbolθ_1,...,\boldsymbolθ_K)$ that formalizes whether a specific set of $K$ sensitivity scenarios $\{\boldsymbolθ_1,...,\boldsymbolθ_K\}$ is adequate to summarize how the OCs of the trial design vary across all plausible values of the UPs. Then, we use optimization techniques to select the best set of simulation scenarios to exemplify the OCs of the trial design.
