Testing for Treatment Effect Twice Using Internal and External Controls in Clinical Trials
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结合内部与外部对照对临床试验处理效应进行双重检验
- 研究问题
- 引入外部对照可能增加检验功效，也可能带来未测偏差；如何量化结论对偏差的敏感性，并控制联合检验的第一类错误？
- 方法与思路
- 作者结合仅用随机试验与加入外部对照的两项相关检验，利用统计量联合分布校正多重检验，并构造未测偏差敏感性分析。
- 材料陈述的贡献
- 摘要称联合检验以随机试验分析为锚点：外部对照有益时可明显增加功效，无益时功效损失较小，并提供理论及实例评估。
- 阅读关注点
- 应核对未测偏差的参数化、两统计量联合分布的推导、错误率分配，以及功效增益和损失结论的具体适用条件。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；摘要未给出偏差模型、校正幅度或实例数值，不能认定加入外部对照总有益。
原文摘要
Leveraging external controls -- relevant individual patient data under control from external trials or real-world data -- has the potential to reduce the cost of randomized controlled trials (RCTs) while increasing the proportion of trial patients given access to novel treatments. However, due to lack of randomization, RCT patients and external controls may differ with respect to covariates that may or may not have been measured. Hence, after controlling for measured covariates, for instance by matching, testing for treatment effect using external controls may still be subject to unmeasured biases. In this paper, we propose a sensitivity analysis approach to quantify the magnitude of unmeasured bias that would be needed to alter the study conclusion that presumed no unmeasured biases are introduced by employing external controls. Whether leveraging external controls increases power or not depends on the interplay between sample sizes and the magnitude of treatment effect and unmeasured biases, which may be difficult to anticipate. This motivates a combined testing procedure that performs two highly correlated analyses, one with and one without external controls, with a small correction for multiple testing using the joint distribution of the two test statistics. The combined test provides a new method of sensitivity analysis designed for data fusion problems, which anchors at the unbiased analysis based on RCT only and spends a small proportion of the type I error to also test using the external controls. In this way, if leveraging external controls increases power, the power gain compared to the analysis based on RCT only can be substantial; if not, the power loss is small. The proposed method is evaluated in theory and power calculations, and applied to a real trial.