Adjusting for covariates in randomized controlled trials can enhance the credibility and efficiency of treatment effect estimation. However, handling numerous covariates and their complex (non-linear) transformations poses a challenge. Motivated by the case study of the Best Apnea Interventions for Research (BestAIR) trial data from the National Sleep Research Resource (NSRR), where the number of covariates (p=114) is comparable to the sample size (N=196), we propose a principled Covariate Adjustment with Variable Selection (COADVISE) framework. COADVISE enables variable selection for covariates most relevant to the outcome while accommodating both linear and nonlinear adjustments. This framework ensures consistent estimates with improved efficiency over unadjusted estimators and provides robust variance estimation, even under outcome model misspecification. We demonstrate efficiency gains through theoretical analysis, extensive simulations, and a re-analysis of the BestAIR trial data to compare alternative variable selection strategies, offering cautionary recommendations. A user-friendly R package, Coadvise, is available to facilitate practical implementation.

翻译：在随机对照试验中进行协变量调整可以提升治疗效果估计的可信度与效率。然而，处理大量协变量及其复杂的（非线性）变换是一个挑战。受美国国家睡眠研究资源库（NSRR）中最佳呼吸暂停干预研究（BestAIR）试验数据的案例启发——该案例中协变量数量（p=114）与样本量（N=196）相当，我们提出了一种基于原则的协变量调整与变量选择（COADVISE）框架。COADVISE能够对与结局最相关的协变量进行变量选择，同时兼容线性和非线性调整。该框架确保了估计的一致性，其效率优于未调整的估计量，并提供了稳健的方差估计，即使在结局模型设定错误的情况下也是如此。我们通过理论分析、广泛的模拟研究以及对BestAIR试验数据的重新分析，展示了效率提升，比较了不同的变量选择策略，并提出了审慎的建议。一个用户友好的R软件包Coadvise已发布，以促进实际应用。