Modern Causal Inference Approaches to Improve Power for Subgroup Analysis in Randomized Controlled Trials
利用现代因果推断提高随机对照试验亚组分析的检验力AI 解读 ↓
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利用现代因果推断提高随机对照试验亚组分析的检验力
- 研究问题
- 小样本随机试验的预设亚组分析如何同时利用基线预测变量和外部数据提高检验力，并应对模型错误及实际正值性不足？
- 方法与思路
- 作者提出兼容多种建模方法的去偏估计，并以协变量平衡、自动 DML 和校准 DML 三种方案处理极端权重与推断不稳定。
- 材料陈述的贡献
- 摘要称多种模拟显示检验力改善，并给出实践建议；应用合并两个小型试验，分析不同未治疗精神病时长亚组的药物效果。
- 阅读关注点
- 应核对外部数据与试验目标之间的可迁移条件、亚组预设方式，以及三种估计量如何处理正值性不足和控制推断误差。
- 阅读边界
- 这是 arXiv 预印本，同行评审或接收未核实。摘要未说明完整识别条件、检验力数值及药效结果，外部数据借用的有效性仍需核对。
原文摘要
Randomized controlled trials (RCTs) often include subgroup analyses to assess whether treatment effects vary across pre-specified patient populations. However, these analyses frequently suffer from small sample sizes which limit the power to detect heterogeneous effects. Power can be improved by leveraging predictors of the outcome -- i.e., through covariate adjustment -- as well as by borrowing external data from similar RCTs or observational studies. The benefits of covariate adjustment may be limited when the trial sample is small. Borrowing external data can increase the effective sample size and improve power, but it introduces two key challenges: (i) integrating data across sources can lead to model misspecification, and (ii) practical violations of the positivity assumption -- where the probability of receiving the target treatment is near-zero for some covariate profiles in the external data -- can lead to extreme inverse-probability weights and unstable inferences, ultimately negating potential power gains. To account for these shortcomings, we present an approach to improving power in pre-planned subgroup analyses of small RCTs that leverages both baseline predictors and external data. We propose debiased estimators that accommodate parametric, machine learning, and nonparametric Bayesian methods. To address practical positivity violations, we introduce three estimators: a covariate-balancing approach, an automated debiased machine learning (DML) estimator, and a calibrated DML estimator. We show improved power in various simulations and offer practical recommendations for the application of the proposed methods. Finally, we apply them to evaluate the effectiveness of citalopram for negative symptoms in first-episode schizophrenia patients across subgroups defined by duration of untreated psychosis, using data from two small RCTs.