Guide for Comparing Estimators of Policy Change Effects on Health.
Guide for Comparing Estimators of Policy Change Effects on Health.
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政策变化对健康影响的估计者比较指南。
DOI：
10.1097/ede.0000000000001586
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Goin,DanaE
中科院分区：
文献类型：
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作者：
Riddell,CorinneA;Goin,DanaE
政策变化对人类健康的影响引起了流行病学家的极大兴趣。政策通常以非随机的方式引入，研究人员必须使用观察数据来估计政策变化的影响。在2020年左右，新的估计量主要在计量经济学文献中提出（流行病学文献中有一篇文章），以估计在差异中差异（DID）设置中对治疗（ATT）的平均治疗效果，其中在治疗单位的不同时间引入政策。双向固定效应估计量（以前是这些设置的标准方法）被证明存在偏倚和/或在治疗效应为动态或异质性时（当效应分别随时间推移或在采用队列中不同时）估计不直观的目标参数。1解决双向固定效应估计量局限性的新引入的估计量包括Callaway和Sant '安娜引入的组时ATT估计量，2 Sun和Abraham引入的队列ATT估计量，3以及Ben-Michael等人为此背景开发的目标试验估计量。4另一种工具，Goodman Bacon分解，1允许研究人员看到传统的双向固定效应估计量如何由于动态效应而产生偏差，或者可以通过使用流行病学家不太可能选择的权重聚合异质效应来估计非直观参数。为了方便流行病学家使用，我们创建了一个在线指南（https：//rpubs. com/corinne-riddell/guide-to-did-estimators）使用R代码计算复杂性不断增加的九种场景中的ATT估计值，并描述了结果。一项政策可以以多种方式推出，如何引入政策及其对健康结果的影响形式在确定哪种估计方法适合分析时很重要。对于任何政策分析，研究人员都可以观察到以下特征，这些特征可能会影响双向固定效应估计量和被估计参数的性能：
The effects of policy changes on human health are of great interest to epidemiologists. Policies are often introduced in a nonrandomized fashion, and researchers must use observational data to estimate the effects of the policy change. Around 2020, new estimators were proposed mostly in the econometric literature (with one article in the epidemiologic literature) to estimate the average treatment effect on the treated (ATT) for difference-in-differences (DID) settings where polices are introduced at different times across treated units. The two-way fixed-effect estimator, which had previously been the standard approach for these settings, was shown to be biased and/or to estimate an unintuitive target parameter when treatment effects were dynamic or heterogeneous (when effects differed over time or across adoption cohorts, respectively). 1 Newly introduced estimators that addressed the limitations of the twoway fixed-effects estimator include the Group-Time ATT estimator introduced by Callaway and Sant’Anna, 2 the Cohort ATT estimator introduced by Sun and Abraham, 3 and the Target Trial estimator developed for this context by Ben-Michael et al. 4 Another tool, the Goodman Bacon decomposition, 1 allows researchers to see how the traditional two-way fixed-effects estimator can be biased as a result of dynamic effects, or can estimate a nonintuitive parameter by aggregating effects across heterogeneous effects using weights that epidemiologists would be unlikely to choose.To illustrate the need for new estimators and facilitate their use by epidemiologists, we created an online guide (https://rpubs. com/corinne-riddell/guide-to-did-estimators) that uses R code to compute the ATT estimates across nine scenarios of increasing complexity and describes the findings. A policy can be rolled out in a multitude of ways, and how the policy is introduced and the shape of its effect on the health outcome matter in determining which estimator is appropriate for the analysis. For any policy analysis, the following characteristics can be observed by the researcher and can affect the performance of the two-way fixedeffects estimator and the parameter being estimated: