AI 中文总结
针对集群规模不等的回切换实验，提出基于CUPAC的功率最优方差缩减方法，经蒙特卡洛模拟验证可提升统计功效。
AI 中文摘要
在集群规模不等的回切换实验中，随机化单元间的结果离散会放大估计量方差，限制统计功效。标准的“将预测结果作为协变量的控制法”（CUPAC）在该场景下的方差缩减效果可能并非最优，因为其基础形式仅关注整体预测准确性，未区分回切换实验中随机化单元间的方差成分与单个观测间的方差成分，而这些成分对估计量方差的贡献并不均等。我们提出一种基于CUPAC的功率最优方差缩减方法，通过平衡随机化单元间及单元内的噪声预测以实现最大统计功效。该方法利用回切换实验处理效应估计量的方差分解框架，调整结果预测与分析时的残差化过程以最小化处理效应方差。本研究首先构建了功率最优CUPAC的理论框架，随后通过大量蒙特卡洛模拟研究验证了该框架，最后讨论了所提方法的实际考量，包括其潜在的效率提升与局限性。
英文摘要
In switchback experiments with unequal cluster sizes, outcome dispersion across randomization units inflates estimator variance and limits statistical power. Standard control-using-prediction-as-covariate (CUPAC) adjustment may be suboptimal for variance reduction in this setting, because in its basic form it targets overall predictive accuracy and does not distinguish between the components of variance that vary across the randomization units of switchback experiments and those that vary across individual observations, even though these components contribute unequally to estimator variance. We propose a power-optimal variance reduction methodology via CUPAC that balances prediction of the noise between and within randomization units to achieve maximum statistical power. The methodology utilizes the framework for decomposition of the variance of the treatment-effect estimator for switchback experiments, and adapts both the outcome prediction and the analysis-time residualization to minimize the treatment-effect variance. The study first develops the theoretical framework for the power-optimal CUPAC. We then validate the theoretical framework through an extensive Monte Carlo simulation study. Finally, we discuss the practical considerations of the proposed methodology, including its potential efficiency gains and limitations.
