Benkeser et al. demonstrate how adjustment for baseline covariates in randomized trials can meaningfully improve precision for a variety of outcome types. Their findings build on a long history, starting in 1932 with R.A. Fisher and including more recent endorsements by the U.S. Food and Drug Administration and the European Medicines Agency. Here, we address an important practical consideration: *how* to select the adjustment approach -- which variables and in which form -- to maximize precision, while maintaining Type-I error control. Balzer et al. previously proposed *Adaptive Prespecification* within TMLE to flexibly and automatically select, from a prespecified set, the approach that maximizes empirical efficiency in small trials (N$<$40). To avoid overfitting with few randomized units, selection was previously limited to working generalized linear models, adjusting for a single covariate. Now, we tailor Adaptive Prespecification to trials with many randomized units. Using $V$-fold cross-validation and the estimated influence curve-squared as the loss function, we select from an expanded set of candidates, including modern machine learning methods adjusting for multiple covariates. As assessed in simulations exploring a variety of data generating processes, our approach maintains Type-I error control (under the null) and offers substantial gains in precision -- equivalent to 20-43\% reductions in sample size for the same statistical power. When applied to real data from ACTG Study 175, we also see meaningful efficiency improvements overall and within subgroups.

翻译：Benkeser等人展示了在随机试验中调整基线协变量如何显著提高多种结局类型的估计精度。其研究继承自1932年R.A. Fisher开创的悠久传统，并延续至美国食品药品监督管理局与欧洲药品管理局近年来的认可。本文针对一个重要实践问题展开：如何选择调整策略（具体变量及其形式）以最大化精度，同时控制第一类错误。Balzer等人先前在TMLE框架中提出自适应预指定方法，通过预设候选策略集灵活自动地选择能在小样本试验（N<40）中最大化经验效率的方法。为避免因随机单元过少导致的过拟合，先前策略仅限于调整单一协变量的工作广义线性模型。现针对含大量随机单元的试验，我们改进了自适应预指定方法：采用V折交叉验证与基于估计影响曲线平方的损失函数，从扩展候选集（含调整多协变量的现代机器学习方法）中进行策略选择。模拟多种数据生成机制的结果表明，本方法在维持第一类错误控制（零假设条件下）的同时，可带来显著精度提升——相当于在同等统计功效下减少20-43%的样本量。当应用于ACTG研究175的真实数据时，整体及亚组分析中的效率均有实质性改善。