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精度。他们的研究建立在自1932年R.A. Fisher起始、并得到美国食品药品监督管理局和欧洲药品管理局近期认可的长期历史基础上。在此，我们解决一个重要的实践问题：*如何*选择调整方法（即选择哪些变量及其具体形式）以最大化精度，同时控制I类错误。Balzer等人先前在TMLE框架内提出*自适应预规范*方法，能从小规模试验（样本量N<40）的预设候选集中灵活自动地选择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，该方法先前仅限制使用调整单一协变量的通用线性模型。现在，我们将自适应预规范方法适配至具有大量随机单元的试验中。通过使用V折交叉验证和以估计的影响曲线平方为损失函数，我们从包含调整多协变量的现代机器学习方法在内的扩展候选集中进行选择。在探索多种数据生成过程的模拟评估中，我们的方法在保持I类错误控制（原假设下）的同时，实现了精度的大幅提升——等效于在相同统计效能下减少20-43%的样本量。当应用于ACTG研究175的真实数据时，我们也观察到整体及亚组分析中效率的实质性改善。