将协变量调整与次要终点信息结合以提高随机试验的精度
Combining covariate adjustment with information from secondary endpoints to improve precision in randomized trials
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中文总结 AI 辅助
本研究将协变量调整与次要终点信息结合，通过扩展结构方程模型并采用交叉验证模型平均法，在随机试验中提升了主要终点平均处理效应估计的精度，且验证了其效率与稳健性的折中效果。
中文摘要 AI 辅助
背景/目的：对预后基线协变量进行调整可提高随机试验的精度。已有研究表明，联合建模主要终点和次要终点可通过在各终点间借用信息获得额外精度。本研究探讨是否可将这些方法结合，以实现仅通过协变量调整无法获得的效率提升。方法：我们扩展了此前提出的用于从次要终点借用信息的单因子结构方程模型框架，将基线协变量纳入其中，同时保留主要终点的平均处理效应作为估计目标。为降低对模型误设的敏感性，我们使用交叉验证模型平均法将该估计量与常规协变量调整估计量相结合。我们在模拟中评估了操作特性，并将方法应用于一项对比极低尼古丁含量香烟与正常尼古丁含量香烟的随机试验。结果：当结构方程模型设定正确时，在所有模拟场景中，终点借用均比常规协变量调整提升了效率。模型误设可能引发偏差和覆盖不足。与结构方程模型估计量相比，模型平均法降低了偏差并改善了覆盖度，尽管在严重误设情况下覆盖度仍不理想。在试验应用中，模型平均估计量比未调整估计量的精度高21%，比仅协变量调整的精度高13%。结论：即使纳入基线协变量后，次要终点仍可提供关于主要终点平均处理效应的有用信息。这些提升需要比常规协变量调整更强的假设；模型平均法为效率与稳健性提供了实用的折中方案。
英文摘要
Background/Aims: Adjustment for prognostic baseline covariates can improve precision in randomized trials. Previous work has shown that jointly modeling primary and secondary endpoints can yield additional precision by borrowing information across endpoints. We investigated whether these approaches can be combined to achieve efficiency gains beyond those obtained through covariate adjustment alone. Methods: We extended a previously proposed one-factor structural equation modeling framework for borrowing information from secondary endpoints to incorporate baseline covariates while retaining the average treatment effect on the primary endpoint as the estimand. To mitigate sensitivity to model misspecification, we combined this estimator with a conventional covariate-adjusted estimator using cross-validated model averaging. We evaluated operating characteristics in simulations and applied the methods to a randomized trial of very low versus normal nicotine content cigarettes. Results: When the structural equation model was correctly specified, endpoint borrowing improved efficiency beyond conventional covariate adjustment across all simulated settings. Model misspecification could induce bias and undercoverage. Model averaging reduced bias and improved coverage relative to the structural equation model estimator, although coverage remained imperfect under severe misspecification. In the trial application, the model-averaged estimate was 21% more precise than the unadjusted estimate and 13% more precise than covariate adjustment alone. Conclusion: Secondary endpoints can contribute meaningful information about the average treatment effect on a primary endpoint even after baseline covariates have been incorporated. These gains require stronger assumptions than conventional covariate adjustment; model averaging provides a practical compromise between efficiency and robustness.
