Difference-in-differences is a popular method for observational health policy evaluation. It relies on a causal assumption that in the absence of intervention, treatment groups' outcomes would have evolved in parallel to those of comparison groups. Researchers frequently look for parallel trends in the pre-intervention period to bolster confidence in this assumption. The popular "parallel trends test" evaluates a null hypothesis of parallel trends and, failing to find evidence against the null, concludes that the assumption holds. This tightly controls the probability of falsely concluding that trends are not parallel but may have low power to detect non-parallel trends. When used as a screening step, it can also introduce bias in treatment effect estimates. We propose a non-inferiority/equivalence approach that tightly controls the probability of missing large violations of parallel trends measured on the scale of the treatment effect. Our framework nests several common use cases, including linear trend tests and event studies. We show that our approach may induce no or minimal bias when used as a screening step under commonly-assumed error structures, and absent violations, can offer a higher-power alternative to testing treatment effects in more flexible models. We illustrate our ideas by re-considering a study of the impact of the Affordable Care Act's dependent coverage provision.

翻译：双重差分法是观察性卫生政策评估中的一种常用方法。该方法依赖于一个因果假设：若无干预，处理组的结果将与对照组的结果平行演变。研究者常在干预前阶段寻找平行趋势，以增强对该假设的信心。常用的"平行趋势检验"评估平行趋势的零假设，若未能找到拒绝零假设的证据，则推断该假设成立。这种方法严格控制了错误得出趋势不平行结论的概率，但可能对检测非平行趋势的效力较低。当用作筛选步骤时，还可能引入处理效应估计的偏差。我们提出一种非劣效性/等效性方法，该方法严格控制了遗漏大规模平行趋势违背（以处理效应尺度衡量）的概率。我们的框架嵌套了若干常见用例，包括线性趋势检验和事件研究。我们证明，在常见假设误差结构下，当作为筛选步骤使用时，我们的方法可能不会引入偏差或仅引入极小偏差；且在没有违背的情况下，可为更灵活模型中的处理效应检验提供更高检验效力的替代方案。我们通过重新审视《平价医疗法案》附属保险条款影响的研究来阐释我们的观点。