In randomized controlled trials, patient characteristics are expected to be well balanced between treatment groups; however, adjustment for characteristics that are prognostic can still be beneficial with a modest gain in statistical power. Nevertheless, previous reviews show that many trials use unadjusted analyses. In this article, we review current practice regarding covariate adjustment in cardiovascular trials among all 84 randomized controlled trials relating to cardiovascular disease published in the New England Journal of Medicine, The Lancet, and the Journal of the American Medical Association during 2019. We identify trials in which use of covariate adjustment led to a change in the trial conclusions. By using these trials as case studies, along with data from the CHARM trial and simulation studies, we demonstrate some of the potential benefits and pitfalls of covariate adjustment. We discuss some of the complexities of using covariate adjustment, including how many covariates to choose, how covariates should be modeled, how to handle missing data for baseline covariates, and how adjusted analyses are viewed by regulators. We conclude that contemporary cardiovascular trials do not make best use of covariate adjustment and that more frequent use could lead to improvements in the efficiency of future trials.

在随机对照试验中，预计治疗组之间的患者特征将得到很好的平衡；然而，对预后特征的调整仍然有益于统计能力的适度增加。然而，之前的评论表明，许多试验使用未经调整的分析。在本文中，我们回顾了发表在《新英格兰医学杂志》、《柳叶刀》和《美国医学会杂志》上的所有 84 项与心血管疾病相关的随机对照试验中心血管试验协变量调整的现行做法在 2019 年期间。我们确定了使用协变量调整导致试验结论发生变化的试验。通过将这些试验作为案例研究，以及来自 CHARM 试验和模拟研究的数据，我们展示了协变量调整的一些潜在好处和缺陷。我们讨论了使用协变量调整的一些复杂性，包括选择多少个协变量、协变量应该如何建模、如何处理基线协变量的缺失数据以及监管机构如何看待调整后的分析。我们得出结论，当代心血管试验并未充分利用协变量调整，更频繁地使用协变量调整可能会提高未来试验的效率。