Combining covariate adjustment with information from secondary endpoints to improve precision in randomized trials
结合协变量调整与次要终点信息提高随机试验精度AI 解读 ↓
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结合协变量调整与次要终点信息提高随机试验精度
- 研究问题
- 在随机试验中，已调整基线协变量后，借用次要终点信息能否进一步提高主要终点平均处理效应的估计精度？
- 方法与思路
- 作者将基线协变量纳入单因子结构方程框架，再通过交叉验证模型平均，结合该估计与常规协变量调整估计。
- 材料陈述的贡献
- 摘要称模型正确时借用终点提高效率；模型平均缓解错设影响，应用中较单纯协变量调整获得百分之十三的精度改善。
- 阅读关注点
- 核对跨终点借用信息所需假设、结构方程约束、模型平均权重选择，以及精度定义和严重错设下的覆盖率。
- 阅读边界
- 这是 arXiv 预印本，同行评审及接收未核实；作者明确指出错设可能导致偏差与欠覆盖，模型平均也未完全消除风险。
原文摘要
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.