早期随机试验中的阴性对照结局调整：估计疫苗对HIV暴露未感染婴儿免疫反应的影响
Negative Control Outcome Adjustment in Early-Phase Randomized Trials: Estimating Vaccine Effects on Immune Responses in HIV Exposed Uninfected Infants
AI总结：
针对早期随机试验中协变量调整的局限，提出阴性对照结局（NCO）调整方法，通过引入与主要结局高度相关的辅助随机化后结局来提高估计精度，并在HIV暴露未感染婴儿疫苗试验的再分析中验证了其有效性。
摘要：
在随机试验中，对预后基线变量进行调整可以减少由协变量不平衡引起的偏差并提高效率。尽管在后期试验中使用协变量调整因其良好的大样本性质而具有合理性，但由于不确定哪些变量具有预后性，以及可能出现的精度损失、I类错误率膨胀和置信区间覆盖率不足，协变量调整很少用于小样本的早期研究。为了解决这个问题，我们考虑对有效的阴性对照结局（NCO）进行调整，NCO是一种辅助的随机化后结局，被认为完全不受治疗影响，但与主要结局的相关性高于基线协变量。我们阐明了允许对NCO进行调整而不产生随机化后选择偏差的假设，并描述了NCO调整能够优于仅对基线协变量进行调整的合理数据生成模型。在数值实验中，我们展示了其性能，并提供了关于模型选择和有限样本方差校正的实用建议。我们将这些方法应用于两项HIV暴露未感染（HEU）婴儿早期疫苗试验的再分析，其中我们证明，与避免调整或仅调整基线协变量的标准方法相比，对辅助基线后免疫学参数进行调整可以提高疫苗效应估计的精度。
英文摘要：
Adjustment for prognostic baseline variables can reduce bias due to covariate imbalance and increase efficiency in randomized trials. While the use of covariate adjustment in late-phase trials is justified by favorable large-sample properties, it is seldom used in small, early-phase studies, due to uncertainty in which variables are prognostic and the potential for precision loss, type I error rate inflation, and undercoverage of confidence intervals. To address this problem, we consider adjustment for a valid negative control outcome (NCO), or an auxiliary post-randomization outcome believed completely unaffected by treatment but more highly correlated with the primary outcome than baseline covariates. We articulate the assumptions that permit adjustment for NCOs without producing post-randomization selection bias, and describe plausible data generating models where NCO adjustment can improve upon adjustment for baseline covariates alone. In numerical experiments, we illustrate performance and provide practical recommendations regarding model selection and finite-sample variance corrections. We apply our methods to the reanalysis of two early-phase vaccine trials in HIV exposed uninfected (HEU) infants, where we demonstrate that adjustment for auxiliary post-baseline immunological parameters can enhance precision of vaccine effect estimates relative to standard approaches that avoid adjustment or adjust for baseline covariates alone.
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