边做边学（LAGO）试验：通过持续学习优化治疗并预防试验失败
Learn-As-you-GO (LAGO) Trials: Optimizing Treatments and Preventing Trial Failure Through Ongoing Learning
AI总结：
本文研究试验进行中调整干预组合导致标准推断失效的问题，将 LAGO 理论扩展至连续结局，给出有效估计、检验及最优干预组合置信集。
摘要：
众所周知，在试验进行期间改变干预组合，使用标准统计方法不会产生有效的推断。然而，在公共卫生实施试验中，通常需要对复杂干预组合进行调整、定制或微调，尤其是在干预组合未产生预期效果时。本文给出了在试验进行期间调整干预组合时，所得分析仍保持有效的条件。我们关于此类“边做边学”（Learn-As-you-GO，LAGO）研究的结果，将遵循 logistic 回归模型的二分类结局 LAGO 理论（Nevo、Lok 和 Spiegelman，2021）扩展到灵活条件均值模型下的连续结局 LAGO。我们推导了干预效应的点估计量和区间估计量，并确保总体干预效应假设检验的有效性。我们为最优干预组合构建了置信集——该组合在达到预先指定的平均结局的同时使成本最小化——并为所有干预组合构成下的平均结局构建了置信带。这项工作将有助于大规模干预试验的设计和分析，在这些试验中，干预组合会在试验进行期间被调整、定制或微调。
英文摘要：
It is well known that changing the intervention package while a trial is ongoing does not lead to valid inference using standard statistical methods. However, it is often necessary to adapt, tailor, or tweak a complex intervention package in public health implementation trials, especially when the intervention package does not have the desired effect. This article presents conditions under which the resulting analyses remain valid even when the intervention package is adapted while a trial is ongoing. Our results on such Learn-As-you-GO (LAGO) studies extend the theory of LAGO for binary outcomes following a logistic regression model (Nevo, Lok and Spiegelman, 2021) to LAGO for continuous outcomes under flexible conditional mean model. We derive point and interval estimators of the intervention effects and ensure the validity of hypothesis tests for an overall intervention effect. We develop a confidence set for the optimal intervention package, which achieves a pre-specified mean outcome while minimizing cost, and confidence bands for the mean outcome under all intervention package compositions. This work will be useful for the design and analysis of large-scale intervention trials where the intervention package is adapted, tailored, or tweaked while the trial is ongoing.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
