Measuring continuous baseline covariate imbalances in clinical trial data
Measuring continuous baseline covariate imbalances in clinical trial data
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DOI：
10.1177/0962280211416038
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发表时间：
2015-04-01
影响因子：
2.3
通讯作者：
Palesch, Yuko Y.
中科院分区：
文献类型：
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作者：
Ciolino, Jody D.;Martin, Renee H.;Palesch, Yuko Y.
本文介绍并比较了几种测量临床试验数据连续基线协变量不平衡的方法。模拟表明，虽然t检验不是评估连续基线协变量不平衡的合适方法，但检验统计量本身是捕获连续协变量分布中不平衡的稳健度量。提出了评估不平衡对偏倚、I型错误率和治疗效果的假设检验对连续结果的影响的指南，并说明了协变量调整分析（ANCOVA）的好处。
This paper presents and compares several methods of measuring continuous baseline covariate imbalance in clinical trial data. Simulations illustrate that though the t-test is an inappropriate method of assessing continuous baseline covariate imbalance, the test statistic itself is a robust measure in capturing imbalance in continuous covariate distributions. Guidelines to assess effects of imbalance on bias, type I error rate and power for hypothesis test for treatment effect on continuous outcomes are presented, and the benefit of covariate-adjusted analysis (ANCOVA) is also illustrated.