Robust Variance Estimation for Covariate-Adjusted Unconditional Treatment Effect in Randomized Clinical Trials with Binary Outcomes.

IF 0.7 Q3 STATISTICS & PROBABILITY Statistical Theory and Related Fields Pub Date : 2023-01-01 Epub Date: 2023-04-28 DOI:10.1080/24754269.2023.2205802
Ting Ye, Marlena Bannick, Yanyao Yi, Jun Shao
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引用次数: 0

Abstract

To improve precision of estimation and power of testing hypothesis for an unconditional treatment effect in randomized clinical trials with binary outcomes, researchers and regulatory agencies recommend using g-computation as a reliable method of covariate adjustment. However, the practical application of g-computation is hindered by the lack of an explicit robust variance formula that can be used for different unconditional treatment effects of interest. To fill this gap, we provide explicit and robust variance estimators for g-computation estimators and demonstrate through simulations that the variance estimators can be reliably applied in practice.

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二元结果随机临床试验中协变量调整无条件治疗效果的稳健方差估计
为了提高在具有二元结果的随机临床试验中无条件治疗效果的估计精度和检验假设的能力,研究人员和监管机构建议使用g计算作为协变量调整的可靠方法。然而,由于缺乏可用于感兴趣的不同无条件治疗效果的显式稳健方差公式,g计算的实际应用受到阻碍。为了填补这一空白,我们为g-computation估计量提供了显式和稳健的方差估计量,并通过仿真证明了方差估计量在实践中可以可靠地应用。
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来源期刊
CiteScore
0.90
自引率
20.00%
发文量
21
期刊最新文献
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