Effect of Covariate Omission in Randomised Controlled Trials: A Review and Simulation Study

Effect of Covariate Omission in Randomised Controlled Trials: A Review and Simulation Study
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随机对照试验中协变量遗漏的影响:回顾和模拟研究

DOI:
10.1111/insr.12468
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发表时间:
2021
影响因子:
2
通讯作者:
Gosho Masahiko
Gosho Masahiko
中科院分区:
数学3区
文献类型:
--
作者:
Ishii Ryota;Maruo Kazushi;Gosho Masahiko

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在随机对照试验(RCT)中,预计分配的治疗组和协变量是独立的。在正态模型的框架中,省略协变量会影响治疗效应估计量的精密度,但不会产生估计量偏倚。然而,当省略协变量时,即使在RCT中,非正态模型(如logistic和考克斯模型)中的治疗效应估计量也存在偏倚。此外,加速失效时间模型和泊松模型中的协变量遗漏导致标准误(SE)估计量出现偏倚,并导致治疗效应的I类错误率膨胀。在本研究中,我们从治疗效应估计量的偏倚和精密度、SE估计量的偏倚以及RCT中假设的这些模型的I类错误率方面综述了关于协变量遗漏影响的文献。此外,我们进行了模拟研究,在各种各样的情况下,以评估协变量遗漏的影响。我们的文献回顾和模拟研究提供了一个简单的指导考虑协变量调整的随机对照试验。我们建议将所有重要协变量和测量协变量纳入分析模型,以减少治疗效应和SE估计值的偏倚,并控制I类错误率。
In randomised controlled trials (RCTs), the allocated treatment group and covariates are expected to be independent. In the framework of the normal model, omitting covariates affects precision of the treatment effect estimator but does not yield bias in the estimator. However, when covariates are omitted, the treatment effect estimator has a bias in non‐normal models such as logistic and Cox models even in RCTs. Additionally, covariate omission in the accelerated failure time model and Poisson model causes a bias in the standard error (SE) estimator and yields an inflation of Type‐I error rate for the treatment effect. In this study, we reviewed the literature regarding the effect of covariate omission from the aspect of the bias and precision of treatment effect estimator, bias of SE estimator, and Type‐I error rate for these models assumed in RCTs. Furthermore, we conducted a simulation study in a wide variety of scenarios to evaluate the effect of covariate omission. Our literature review and simulation study provide a simple guide for consideration of covariate adjustment in RCTs. We recommend that all important and measurement covariates be included in analysis models to reduce bias for the treatment effect and SE estimators and control the Type‐I error rate.
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