Bias reduction in g-computation for covariate adjustment in randomized clinical trials
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随机临床试验中协变量调整 g-computation 的偏差减少
- 研究问题
- 随机试验样本或事件数相对协变量较少时，如何减少 g-computation 的估计偏差与方差低估，并处理最大似然估计不存在的问题？
- 方法与思路
- 作者分析渐近偏差，借助广义 Oaxaca–Blinder 估计构造有界去偏估计量，修改最大似然或 Firth 干扰估计，并调整小样本方差。
- 材料陈述的贡献
- 摘要称新方法在模拟中改善有限样本估计与推断；试验再分析中，在标准回归无法拟合的小子群提高了估计精度。
- 阅读关注点
- 应核对目标治疗效应的尺度、有界性来源和偏差修正公式，以及 Firth 修改、方差调整与小子群分析的具体条件和比较。
- 阅读边界
- 这是 arXiv 预印本，同行评审及接收未核实；摘要未列出适用结局、正则条件和改善幅度，不能由单个案例推断普遍有限样本优势。
原文摘要
G-computation is a powerful method for estimating unconditional treatment effects with covariate adjustment in randomized clinical trials. It typically relies on fitting canonical generalized linear models. However, this could be problematic when the sample size or event number is small relative to the number of covariates. Common issues include the underestimation of the variance and the potential nonexistence of maximum likelihood estimators. Bias reduction methods are commonly employed to address these issues, including Firth correction, which guarantees the existence of corresponding estimates. Yet, their application within g-computation remains underexplored. In this article, we analyze the asymptotic bias of g-computation estimators and propose a novel bias-reduction method that improves both estimation and inference. Our approach performs bias correction via generalized Oaxaca-Blinder estimators, and thus the resulting estimators are guaranteed to be bounded. The proposed debiased estimators use slightly modified versions of maximum likelihood or Firth correction estimators for nuisance parameters. We also introduce a simple small-sample bias adjustment for variance estimation to improve finite-sample inference validity. Through extensive simulations, we demonstrate that our proposed method offers superior finite-sample performance, effectively addressing the bias-efficiency tradeoff. Finally, we illustrate its practical utility by reanalyzing a completed randomized clinical trial. In this example, our method improves precision in a small subgroup analysis for which the standard method fails to fit the regression model.