Covariate adjustment is a general method for improving precision when estimating treatment effects in randomized trials and is recommended by the FDA in its 2023 guidance when baseline variables are prognostic for the primary outcome. We focus on a method highlighted in that guidance called ``standardization" (or ``g-computation") for estimating the marginal treatment effect. We address the question of how to reliably estimate variance for binary outcomes when marginal outcome probabilities are close to 0 or 1. We propose an influence function-based leave-one-out cross-validated (IF-LOO) variance estimator for the standardized difference-in-means average treatment effect. Through simulation studies, we show that this estimator provides appropriate type-I error control and performs reliably in challenging settings where existing methods can yield inflated type-I error or fail entirely, such as when outcome events are rare or sample sizes are small. In addition to having desirable statistical properties, we derive a closed-form expression for the proposed estimator, enabling straightforward and reliable implementation by study statisticians. The robust finite-sample performance and ease of implementation suggest the IF-LOO variance estimator is a prudent default choice for standardization in clinical trials.

翻译：协变量调整是随机试验中提高治疗效果估计精度的通用方法，美国食品药品监督管理局（FDA）在其2023年指南中推荐在基线变量对主要结局具有预测作用时使用该方法。我们聚焦于该指南中强调的方法——"标准化"（或"g-计算"）来估计边际处理效应。我们探讨了当边际结局概率接近0或1时，如何可靠估计二分类结果方差的问题。提出了一种基于影响函数的留一交叉验证（IF-LOO）方差估计量，用于标准化均值差的平均处理效应。通过模拟研究，我们证明该估计量能提供适当的I型错误控制，并在现有方法可能导致I型错误膨胀或完全失效的挑战性场景中（如结局事件罕见或样本量较小）表现可靠。除了具有理想的统计性质外，我们还推导出所提估计量的闭式表达式，使研究统计人员能够直接可靠地进行实施。稳健的有限样本性能与简便的实施性表明，IF-LOO方差估计量可作为临床试验标准化的审慎默认选择。