Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.
Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, USA.
Int J Epidemiol. 2024 Oct 13;53(6). doi: 10.1093/ije/dyae137.
The limitations of the two-way fixed effects for the impact evaluation of interventions that occur at different times for each group have meant that 'staggered interventions' have been highlighted in recent years in the econometric literature and, more recently, in epidemiology. Although many alternative strategies (such as staggered difference-in-differences) have been proposed, the focus has predominantly been on scenarios in which one or more control groups are available. However, control groups are often unavailable, due to limitations in the available data or because all units eventually receive the intervention. In this context, interrupted time series (ITS) designs can constitute an appropriate alternative. The extent to which common model specifications for ITS analyses are limited in the case of staggered interventions remains an underexplored area in the methodological literature. In this work, we aim to demonstrate that standard ITS model specifications typically yield biased results for staggered interventions and we propose alternative model specifications that were inspired by recent developments in the difference-in-differences literature to propose adapted analytical strategies.
双向固定效应在评估针对不同组别在不同时间发生的干预措施的影响方面存在局限性，这意味着近年来在计量经济学文献中，以及最近在流行病学领域中，“交错干预”受到了关注。尽管已经提出了许多替代策略（例如交错差分法），但重点主要集中在有一个或多个对照组可用的情况下。然而，由于可用数据的限制或因为所有单位最终都接受了干预措施，对照组往往不可用。在这种情况下，中断时间序列（ITS）设计可以作为一种合适的替代方案。在交错干预的情况下，ITS 分析的常见模型规范在多大程度上受到限制，这在方法学文献中仍然是一个未被充分探索的领域。在这项工作中，我们旨在证明标准的 ITS 模型规范通常会导致交错干预的结果产生偏差，并提出了受差异分析文献中最新发展启发的替代模型规范，以提出适应性分析策略。
