When analyzing data from randomized clinical trials, covariate adjustment can be used to account for chance imbalance in baseline covariates and to increase precision of the treatment effect estimate. A practical barrier to covariate adjustment is the presence of missing data. In this paper, in the light of recent theoretical advancement, we first review several covariate adjustment methods with incomplete covariate data. We investigate the implications of the missing data mechanism on estimating the average treatment effect in randomized clinical trials with continuous or binary outcomes. In parallel, we consider settings where the outcome data are fully observed or are missing at random; in the latter setting, we propose a full weighting approach that combines inverse probability weighting for adjusting missing outcomes and overlap weighting for covariate adjustment. We highlight the importance of including the interaction terms between the missingness indicators and covariates as predictors in the models. We conduct comprehensive simulation studies to examine the finite-sample performance of the proposed methods and compare with a range of common alternatives. We find that conducting the proposed adjustment methods generally improves the precision of treatment effect estimates regardless of the imputation methods when the adjusted covariate is associated with the outcome. We apply the methods to the Childhood Adenotonsillectomy Trial to assess the effect of adenotonsillectomy on neurocognitive functioning scores.

翻译：在分析随机临床试验数据时，协变量调整可用于校正基线协变量的偶然不平衡性，并提高治疗效果估计的精度。协变量调整面临的一个实际障碍是数据缺失问题。本文基于近期理论进展，首先回顾了几种针对协变量数据不完整时的调整方法。我们探究了缺失数据机制对估计连续型或二分类结局随机临床试验中平均治疗效果的影响。同时，我们考察了结局数据完全观测或随机缺失的情况：针对后者，提出了一种结合逆概率加权调整缺失结局与重叠加权调整协变量的全加权方法。我们强调在模型中纳入缺失指示变量与协变量的交互项作为预测变量的重要性。通过综合模拟研究检验所提方法的有限样本性能，并与多种常见替代方法进行比较。研究发现，当调整的协变量与结局相关时，无论采用何种插补方法，执行所提调整方法通常能提升治疗效果估计的精度。我们将该方法应用于儿童腺样体扁桃体切除术试验，评估该手术对神经认知功能评分的影响。