在随机临床试验中通过GLM调整众多协变量：Jackknife降偏及实用指导
Adjusting for Many Covariates in Randomized Clinical Trials with GLMs: Bias Reduction by Jackknife and Practical Guidance
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中文总结 AI 辅助
针对随机临床试验中协变量过多导致偏差的问题，提出基于jackknife的JASA及校准版JASACal方法，避免样本分割，可调整远超现有基准的协变量数量，并提供实用指导。
中文摘要 AI 辅助
调整基线协变量已成为分析随机临床试验的标准做法。在低维设置中，人们已充分理解，通过参数化工作模型进行协变量调整有时比未调整的均值差估计量更有效。然而，当调整的协变量数量相对于样本量$n$较大时，朴素调整可能引入过多偏差，导致统计推断无效。当前试图解决该问题的文献要么局限于线性工作模型，要么依赖于样本分割，这可能引发对随机对照试验分析可复现性的担忧。在本文中，我们设计了一种新颖的基于jackknife的广义线性模型（GLM）协变量调整方法，我们称之为基于Jackknife得分的调整（JASA），以及其校准版本JASACal。通过采用精细的jackknife策略，JASA和JASACal避免了样本分割并充分利用数据，同时确保即使调整的协变量数量相对于$n$较大时，JASA或JASACal的偏差仍可忽略不计。JASA还涵盖了通过线性工作模型的最先进调整估计量作为特例。通过广泛的模拟实验和真实数据分析，我们证明JASA或JASACal能够调整比现有基准多得多的协变量。这些实证结果也为GLM协变量调整的实用指导提供了新的见解。JASA和JASACal均已纳入我们的R包HOIFCar，可从CRAN获取。开发HOIFCar包旨在为随机对照试验中的协变量调整提供用户友好的选项，特别是当实践者希望调整大量协变量时。
英文摘要
Adjusting for baseline covariates has become standard practice in analyzing randomized clinical trials. In the low-dimensional setting, it is well understood that covariate adjustment through a parametric working model can sometimes be more efficient than the unadjusted difference-in-mean estimator. However, when the number of adjusted covariates is large relative to the sample size $n$, a naïve adjustment may introduce excessive bias, leading to invalid statistical inference. The current literature that tries to resolve this issue is either limited to linear working models or relies on sample splitting, which may raise concerns about the replicability of RCT analyses. In this paper, we devise a novel jackknife-based approach to covariate adjustment through generalized linear models (GLMs), which we term as JAckknife Score-based Adjustment (JASA), together with its calibrated version JASACal. By employing a nuanced jackknife strategy, JASA and JASACal avoid sample splitting and make full use of the data, while ensuring that the bias of JASA or JASACal is still negligible even when the number of adjusted covariates is large compared to $n$. JASA also encompasses state-of-the-art adjusted estimators through linear working models as a special case. Through extensive simulation experiments and a real data analysis, we demonstrate that JASA or JASACal can adjust for a much greater number of covariates than existing benchmarks. These empirical results also shed some new light on practical guidance for covariate adjustment with GLMs. Both JASA and JASACal have been incorporated into our R package HOIFCar available from CRAN. The package HOIFCar is developed to serve as a user-friendly option for covariate adjustment in RCTs, in particular when practitioners hope to adjust for a large number of covariates.
发表机构
- Shanghai Jiao Tong University（上海交通大学）
- Yale University（耶鲁大学）
机构由 AI 辅助整理，请以论文原文为准。
