AI 中文总结
本文探讨监管试验协变量调整的最佳实践，在FDA指南基础上，提出数据自适应协变量调整的实用建议，以提升分析精度并保留统计有效性，推动其在随机试验中应用。
AI 中文摘要
尽管随机化支持在随机试验中使用未调整的效应估计量，但人们对协变量调整以提高精度的兴趣日益浓厚。调整对结局具有预后作用的基线变量可降低估计量方差，从而得到更窄的置信区间和更高的统计功效。美国食品药品监督管理局（U.S. Food and Drug Administration）近期的指南支持使用参数回归模型对预后协变量进行固定调整，但该指南未涉及使用数据自适应或机器学习方法的更灵活方法。本文针对协变量调整以提高分析精度提出了观点，重点关注结局缺失极少的试验中目标人群平均效应的估计，对未调整的效应估计量、固定调整的效应估计量及数据自适应调整的效应估计量提供非技术性概述，为开展数据自适应调整分析提供实用建议，这些分析需完全预先指定、透明可重复实施、对模型误设具有稳健性，且相对于未调整分析能保证提高精度，同时保留统计有效性和所关注的因果效应，希望本文的观点能促进更广泛的讨论，最终推动原则性、预先指定的数据自适应协变量调整在随机试验中得到认可。
英文摘要
While randomization justifies the use of unadjusted effect estimators in randomized trials, there is growing interest in covariate adjustment to improve precision. Adjusting for baseline variables that are prognostic of the outcome can reduce estimator variance, resulting in narrower confidence intervals and increased statistical power. Recent guidance by the U.S. Food and Drug Administration supports fixed adjustment for prognostic covariates using parametric regression models. However, this guidance does not address more flexible approaches using data-adaptive or machine learning methods. We offer our perspectives on covariate adjustment to improve analytic precision. We focus on estimating the average effect for the target population in trials with minimal outcome missingness. We provide a non-technical overview of effect estimators that are unadjusted and effect estimators using fixed versus data-adaptive adjustment. We offer practical suggestions for conducting adjusted analyses that are data-adaptive, fully pre-specified, transparently and reproducibly implemented, robust to model misspecification, and guaranteed to improve precision relative to unadjusted analyses --- all while preserving statistical validity and the causal effect of interest. We hope that sharing our perspectives will foster broader discussion and eventual acceptance of principled, pre-specified, data-adaptive covariate adjustment in randomized trials.
Comments22 pages, including title page and references
