具有干扰性中断的计数数据的中断时间序列分析
Interrupted Time Series Analysis Of Count Data With Nuisance Interruptions
中文总结 AI 辅助
研究如何用中断时间序列分析模拟政策等干预对公共卫生的影响，针对其依赖单研究单元无法调整其他中断的问题，提出用贝叶斯堆叠对干扰性中断影响进行反事实预测，并用于估计德克萨斯州堕胎禁令影响。
中文摘要 AI 辅助
中断时间序列分析已被用于通过使用干预前的数据预测干预期间的反事实时间序列来模拟政策和其他干预对公共卫生的影响。然而，由于通常依赖单个研究单元，这种方法存在无法调整干预期间之前和同时发生的其他中断的风险。COVID-19大流行就是当代公共卫生研究中这种干扰性中断现象的一个突出例子。为了解决这一复杂性，我们建议对干扰性中断的影响使用一系列函数形式进行贝叶斯堆叠，以便对干预期进行反事实预测。我们使用所提出的方法来估计2021年德克萨斯州六周堕胎禁令对德克萨斯州女性记录在案的怀孕情况的影响，同时调整COVID-19大流行的影响。
英文摘要
Interrupted time series analysis has been used to model the effect of policy and other interventions on public health by forecasting a counterfactual time series during the intervention period using data from prior to the intervention. However, due to typically relying on a single study unit, this approach risks not adjusting for other interruptions that precede and co-occur during the intervention period. The COVID-19 pandemic is a prominent example of this phenomenon of nuisance interruptions in contemporary public health research. To address this complication, we propose using Bayesian stacking over a range of functional forms for the impact of the nuisance interruption in order to make counterfactual forecasts for the intervention period. We used our proposed methods to estimate the impact of the 2021 Texas six-week abortion ban on documented pregnancies among women in Texas while adjusting for the impact of the COVID-19 pandemic.
