关于在协变量众多的小型随机对照试验中使用G计算的研究
On the use of G-computation in small randomized controlled trials with many covariates
- Ghent University（根特大学）
- Debre Markos University（德布雷马尔科斯大学）
- Vrije Universiteit Brussel（布鲁塞尔自由大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究探讨在协变量众多的小型随机对照试验中G计算估计量的偏差，提出高维理论并模拟评估补救措施，为有限样本下应用提供指导。
AI中文摘要：
在当代随机对照试验（RCTs）中，患者数量相对于收集的基线协变量数量往往较少。在这种情况下，广义线性模型中条件治疗效应的最大似然估计量及其标准误差可能表现出显著偏差。本研究考察了边际治疗效应的G计算估计量是否会出现类似偏差，该估计量在应用于规范广义线性模型（GLMs）时以其对模型误设的稳健性而闻名。我们借鉴了关于比例渐近机制下G计算的最新文献，提出了理论见解，在该机制下协变量数量随样本量增长。具体而言，我们刻画了在高维设置下标准G计算估计量和留一法G计算估计量的偏差。我们使用线性和逻辑结果模型进行蒙特卡洛模拟，以评估G计算估计量的实用补救措施，包括协变量选择、交叉拟合、留一法交叉拟合以及标准误差的小样本校正。通过对BestAIR试验数据的重新分析，我们获得了进一步的见解。我们的研究结果为在现代随机对照试验中应用G计算提供了指导，特别是在应对有限样本量带来的挑战时。\n关键词：协变量调整，协变量选择，高维，比例渐近，目标学习，交叉拟合。
英文摘要：
In contemporary randomized controlled trials (RCTs), the number of patients is often small relative to the number of baseline covariates collected. In such settings, maximum likelihood estimators of conditional treatment effects in generalized linear models, along with their standard errors, may exhibit substantial bias. This study examines whether similar bias arises in G-computation estimators of marginal treatment effects, which are known for their robustness to model misspecification when applied with canonical GLMs. We develop theoretical insights, drawing on recent literature on G-computation under proportional asymptotic regimes, in which the number of covariates grows with the sample size. Specifically, we characterize the bias of the standard G-computation estimator and the leave-one-out G-computation estimator under such high-dimensional settings. Monte Carlo simulations using linear and logistic outcome models are conducted to evaluate practical remedies for G-computation estimators, including covariate selection, cross-fitting, leave-one-out cross-fitting, and small sample corrections to standard errors. Further insights are derived from a re-analysis of the BestAIR trial data. Our findings provide guidance for the application of G-computation in modern RCTs, particularly when addressing challenges posed by limited sample size. \textbf{Keywords:} Covariate adjustment, covariate selection, high-dimensional, proportional asymptotics, targeted learning, cross-fitting.
