A Robust Score Test in G-Computation for Covariate Adjustment in Randomized Clinical Trials Leveraging Different Variance Estimators via Influence Functions.

IF 1.8 4区 医学 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Xin Zhang, Haitao Chu, Lin Liu, Satrajit Roychoudhury
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引用次数: 0

Abstract

G-computation has become a widely used robust method for estimating unconditional (marginal) treatment effects with covariate adjustment in the analysis of randomized clinical trials. Statistical inference in this context typically relies on the Wald test or Wald interval, which can be easily implemented using a consistent variance estimator. However, existing literature suggests that when sample sizes are small or when parameters of interest are near boundary values, Wald-based methods may be less reliable due to type I error rate inflation and insufficient interval coverage. In this article, we propose a robust score test for g-computation estimators in the context of two-sample treatment comparisons. The proposed test is asymptotically valid under simple and stratified (biased-coin) randomization schemes, even when regression models are misspecified. These test statistics can be conveniently computed using existing variance estimators, and the corresponding confidence intervals have closed-form expressions, making them convenient to implement. Through extensive simulations, we demonstrate the superior finite-sample performance of the proposed method. Finally, we apply the proposed method to reanalyze a completed randomized clinical trial. The new analysis using our proposed score test achieves statistical significance, whilst reducing the issue of type I error inflation.

通过影响函数利用不同方差估计量的随机临床试验中协变量调整的g计算的稳健性得分检验。
在随机临床试验分析中,g计算已成为一种广泛使用的估计无条件(边际)治疗效果并进行协变量调整的稳健方法。这种情况下的统计推断通常依赖于Wald检验或Wald区间,这可以使用一致方差估计器轻松实现。然而,现有文献表明,当样本量较小或感兴趣的参数接近边界值时,由于I型错误率膨胀和区间覆盖不足,基于wald的方法可能不太可靠。在本文中,我们在两样本处理比较的背景下提出了g计算估计器的稳健分数检验。所提出的检验在简单和分层(偏硬币)随机化方案下是渐近有效的,即使在回归模型被错误指定的情况下也是如此。这些检验统计量可以使用现有的方差估计量方便地计算,并且相应的置信区间具有封闭形式的表达式,便于实现。通过大量的仿真,我们证明了所提出的方法具有优越的有限样本性能。最后,我们应用提出的方法来重新分析一个完成的随机临床试验。使用我们提出的分数测试的新分析实现了统计显著性,同时减少了I型错误膨胀的问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Statistics in Medicine
Statistics in Medicine 医学-公共卫生、环境卫生与职业卫生
CiteScore
3.40
自引率
10.00%
发文量
334
审稿时长
2-4 weeks
期刊介绍: The journal aims to influence practice in medicine and its associated sciences through the publication of papers on statistical and other quantitative methods. Papers will explain new methods and demonstrate their application, preferably through a substantive, real, motivating example or a comprehensive evaluation based on an illustrative example. Alternatively, papers will report on case-studies where creative use or technical generalizations of established methodology is directed towards a substantive application. Reviews of, and tutorials on, general topics relevant to the application of statistics to medicine will also be published. The main criteria for publication are appropriateness of the statistical methods to a particular medical problem and clarity of exposition. Papers with primarily mathematical content will be excluded. The journal aims to enhance communication between statisticians, clinicians and medical researchers.
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