A comparison of methods for estimating the average treatment effect on the treated for externally controlled trials
外部对照试验中处理组平均处理效应估计方法的比较AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要直接比较外部对照下的 ATT 因果估计，并分析正值性与无混杂性违背，因此归入因果推断这一最直接方向。
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外部对照试验中处理组平均处理效应估计方法的比较
- 研究问题
- 单臂临床试验使用外部对照时，不同 ATT 估计方法在识别条件成立或违背的情况下，如何权衡偏差、标准差和检验错误？
- 方法与思路
- 作者在不同识别假设情形下模拟比较回归、匹配、加权、双重稳健及结合 SuperLearner 的方法，评价偏差、均方误差等指标。
- 材料陈述的贡献
- 摘要称双重稳健方法通常偏差较小，非匹配方法通常标准差较小，但均方误差没有稳定胜者；识别假设违背显著影响表现。
- 阅读关注点
- 应核对外部对照可比性、正值性与无混杂性的模拟设定，以及各方法调参、区间构造和第一类错误比较是否一致。
- 阅读边界
- 本文为 arXiv 预印本，同行评审及期刊接收未核实；所给摘要含省略说明，未列完整情景，不能把模拟表现当作识别条件的保证。
原文摘要
While randomized trials may be the gold standard for evaluating the effectiveness of the treatment intervention, in some special circumstances, single-arm clinical trials utilizing external control may be considered. The causal treatment effect of interest for single-arm trials is usually the average treatment effect on the treated (ATT) rather than the average treatment effect (ATE). Although methods have been developed to estimate the ATT, the selection and use of these methods require a thorough comparison and in-depth understanding of the advantages and disadvantages of these methods. In this study, we conducted simulations under different identifiability assumptions to compare the performance metrics (e.g., bias, standard deviation (SD), mean squared error (MSE), type I error rate) for a variety of methods, including the regression model, propensity score matching (PSM), Mahalanobis distance matching (MDM), coarsened exact matching, inverse probability weighting, augmented inverse probability weighting (AIPW), AIPW with SuperLearner, and targeted maximum likelihood estimator (TMLE) with SuperLearner. Our simulation results demonstrate that the doubly robust methods in general have smaller biases than other methods. In terms of SD, nonmatching methods in general have smaller SDs than matching-based methods. The performance of MSE is a trade-off between the bias and SD, and no method consistently performs better in term of MSE. The identifiability assumptions are critical to the models' performance: Violation of the positivity assumption can lead to a significant inflation of type I errors in some methods; violation of the unconfoundedness assumption can lead to a large bias for all methods... (Further details are available in the main body of the paper).