标题： 混合控制试验中二元结果的稳健估计和推理：非小细胞肺癌案例研究 显示英文标题

标题： Robust Estimation and Inference in Hybrid Controlled Trials for Binary Outcomes: A Case Study on Non-Small Cell Lung Cancer

摘要： 混合控制试验（HCTs）通过引入外部对照（ECs）来增强随机对照试验（RCTs），近年来越来越多地受到关注，作为一种解决临床研究中功效不足、入组缓慢和伦理问题的方法。然而，从外部对照中借用数据会引发估计和推断的关键统计挑战，特别是在二分类结局的情况下，隐藏偏差更难检测，并且风险差、风险比和优势比等参数是主要感兴趣的指标。 我们提出了一种新颖的框架，该框架结合了不同参数的双重稳健估计量以及外部对照下的协变量偏移，与符合性选择性借用（CSB）相结合，以解决结局不可比较的问题。CSB 使用基于最近邻的符合性得分及其标签条件扩展来进行有限样本精确个体级外部对照选择，解决了二分类结局中的信息有限问题。 为了确保在检验治疗效果时严格控制I型错误率的同时获得更高的功效，我们使用Fisher随机化检验，以CSB估计量作为检验统计量。 广泛的模拟实验表明，我们的方法表现稳健。我们将该方法应用于CALGB 9633和国家癌症数据库的数据，以评估IB期非小细胞肺癌患者化疗的效果，结果显示所提出的方法有效缓解了全借用方法引入的隐藏偏差，严格控制了I型错误率，并提高了相对于仅RCT分析的功效。 显示英文摘要

摘要： Hybrid controlled trials (HCTs), which augment randomized controlled trials (RCTs) with external controls (ECs), are increasingly receiving attention as a way to address limited power, slow accrual, and ethical concerns in clinical research. However, borrowing from ECs raises critical statistical challenges in estimation and inference, especially for binary outcomes where hidden bias is harder to detect and estimands such as risk difference, risk ratio, and odds ratio are of primary interest. We propose a novel framework that combines doubly robust estimators for various estimands under covariate shift of ECs with conformal selective borrowing (CSB) to address outcome incomparability. CSB uses conformal inference with nearest-neighbor-based conformal scores and their label-conditional extensions to perform finite-sample exact individual-level EC selection, addressing the limited information in binary outcomes. To ensure strict type I error rate control for testing treatment effects while gaining power, we use a Fisher randomization test with the CSB estimator as the test statistic. Extensive simulations demonstrate the robust performance of our methods. We apply our method to data from CALGB 9633 and the National Cancer Database to evaluate chemotherapy effects in Stage IB non-small-cell lung cancer patients and show that the proposed method effectively mitigates hidden bias introduced by full-borrowing approaches, strictly controls the type I error rate, and improves the power over RCT-only analysis.