Supplementary material: Quantitative bias analysis for external control arms using real-world data in clinical trials: a primer for clinical researchers

These are peer-reviewed data and glossary for the article 'Quantitative bias analysis for external control arms using real-world data in clinical trials: a primer for clinical researchers' published in the Journal of Comparative Effectiveness Research.Full R code for implementing QBA of missing ECOG data and obtaining E-values for unknown confoundersGlossaryDevelopment of medicines in rare oncologic patient populations are growing, but well-powered randomized controlled trials are typically extremely challenging or unethical to conduct in such settings. External control arms using real-world data are increasingly used to supplement clinical trial evidence where no or little control arm data exists. The construction of an external control arm should always aim to match the population, treatment settings and outcome measurements of the corresponding treatment arm. Yet, external real-world data is typically fraught with limitations including missing data, measurement error and the potential for unmeasured confounding given a nonrandomized comparison. Quantitative bias analysis (QBA) comprises a collection of approaches for modelling the magnitude of systematic errors in data which cannot be addressed with conventional statistical adjustment. Their applications can range from simple deterministic equations to complex hierarchical models. QBA applied to external control arm represent an opportunity for evaluating the validity of the corresponding comparative efficacy estimates. We provide a brief overview of available QBA approaches and explore their application in practice. Using a motivating example of a comparison between pralsetinib single-arm trial data versus pembrolizumab alone or combined with chemotherapy real-world data for RET fusion-positive advanced non-small cell lung cancer (aNSCLC) patients (1–2% among all NSCLC), we illustrate how QBA can be applied to external control arms. We illustrate how QBA is used to ascertain robustness of results despite a large proportion of missing data on baseline ECOG performance status and suspicion of unknown confounding. The robustness of findings is illustrated by showing that no meaningful change to the comparative effect was observed across several ‘tipping-point’ scenario analyses, and by showing that suspicion of unknown confounding was ruled out by use of E-values. Full R code is also provided.

本数据集包含发表于《比较有效性研究杂志》的论文《在临床试验中使用真实世界数据进行外部对照臂的定量偏差分析：临床研究人员的入门指南》的同行评审数据和术语表。全文R代码用于实现缺失ECOG数据的QBA以及获取未知混杂因素的E值。术语表：在罕见肿瘤学患者群体中，药物的开发正在增长，但在这些环境中进行具有足够统计功效的随机对照试验通常极为困难或不道德。因此，外部对照臂使用真实世界数据越来越多地被用于补充临床试验证据，尤其是在缺乏或少量对照臂数据的情况下。构建外部对照臂的目标始终应旨在匹配相应治疗臂的受试者人群、治疗设置和结局测量。然而，外部真实世界数据通常充满局限，包括数据缺失、测量误差以及由于非随机比较而可能出现的未测量混杂因素。定量偏差分析（QBA）包括一系列用于建模数据中系统性误差程度的方法，这些误差无法通过传统的统计调整来解决。其应用范围可以从简单的确定性方程式到复杂的层次模型。将QBA应用于外部对照臂代表了一种评估相应比较有效性估计有效性的机会。我们简要概述了可用的QBA方法，并探讨了其在实践中的应用。以比较普拉塞替尼单臂试验数据与帕博利珠单抗单药或联合化疗的真实世界数据（在所有非小细胞肺癌患者中占1%-2%）用于RET融合阳性晚期非小细胞肺癌（aNSCLC）患者为例，我们展示了如何将QBA应用于外部对照臂。我们通过展示在基线ECOG表现状态存在大量数据缺失和未知混杂因素的怀疑情况下，QBA如何确保结果的稳健性。通过展示在多个“临界点”情景分析中未观察到比较效应的实质性变化，并通过使用E值排除未知混杂因素的怀疑，说明了发现结果的稳健性。同时，我们还提供了完整的R代码。