AI 中文总结
该研究针对存在早期采用的政策场景，提出考虑早期采用的两阶段合成控制方法，经模拟验证可降低偏差，应用于PDMP政策分析后发现其对阿片类药物分发的减少效应无统计显著性。
AI 中文摘要
要求组织使用新系统的政策（如处方药物监测计划（PDMPs））通常分阶段实施，初始阶段为自愿使用期，之后转为强制合规。这使得政策干预可在合规要求生效前被采用（即早期采用），导致结果在强制要求生效前就发生变化。当存在早期采用时，合成控制方法（SCM）所基于的无预期假设会被违反，进而导致政策效果估计出现偏差。我们在交错政策实施的潜在结果框架中对早期采用进行形式化，并将总政策效果分解为早期采用成分与强制成分。随后，我们提出一种两阶段、考虑早期采用的SCM程序：首先使用适配强制前数据的交互式固定效应模型估计早期采用效果，再对结果进行残差处理，之后应用SCM变体估计强制效果与总政策效果。模拟实验（包含早期采用与潜在因子相关的设定）表明，与传统SCM估计量相比，该程序的偏差更小且不确定性量化更优。我们将该框架应用于州级PDMP政策及人均阿片类药物分发数据。在考虑早期采用后，估计结果显示PDMP启用及强制要求实施后阿片类药物分发有所减少，但估计值精度不足，且无统计显著性。
英文摘要
Policies that require organizations to use new systems, such as prescription drug monitoring programs (PDMPs), are often implemented in phases, with an initial period of voluntary access followed by mandated compliance. This allows the policy intervention to be adopted before compliance is required (early adoption), causing outcomes to change before the mandate takes effect. When early adoption is present, the no-anticipation assumption underlying synthetic control methods (SCM) is violated, leading to biased policy effect estimates. We formalize early adoption in a potential outcomes framework for staggered policy implementation and decompose the total policy effect into early adoption and mandate components. We then propose a two-stage, early adoption-aware SCM procedure that first estimates early adoption effects using an interactive fixed effects model fit to pre-mandate data and then residualizes outcomes before applying SCM variants to estimate mandate and total policy effects. Simulations, including settings with correlation between early adoption and latent factors, show reduced bias and improved uncertainty quantification relative to conventional SCM estimators. We apply the framework to state-level PDMP policies and per-capita opioid dispensing. After accounting for early adoption, estimates suggest reductions in opioid dispensing following PDMP availability and mandates; however, the estimates are imprecise and not statistically significant.
