A New Tool for Case Studies in Epidemiology-the Synthetic Control Method.
A New Tool for Case Studies in Epidemiology-the Synthetic Control Method.
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DOI：
10.1097/ede.0000000000000837
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发表时间：
2018-07
期刊：
影响因子：
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通讯作者：
Basu S
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文献类型：
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作者：
Rehkopf DH;Basu S
鉴于该领域病例控制方法的发展，流行病学家可能比大多数学科都敏锐地意识到适当选择控制措施的重要性，这是使用综合控制方法做出的关键决定。用户选择符合条件的控制区域池。 Kakawa等人1适当地只选择符合条件的州，并且由于处理是废除一项政策，因此他们仅限于目前有该政策的其他8个州。这种方法与病例对照研究中就感兴趣的结果选择对照人群最为一致，从那些有结果风险的州中随机选择。 7 最近的综合控制方法应用在供体库中使用的区域少至 168 个，多至 1309 个。此外，根据 Kakawa 等人的表 2 所示的匹配，8 个州中有一半不是很好的匹配，几乎所有结果的对照州都由伊利诺伊州、爱荷华州、密歇根州和北卡罗来纳州组成。合成控制的主要优势是采用数据驱动的方法来获得治疗区域和控制区域之间的紧密预处理匹配，但当供体池状态样本较小时，利用这一优势更具挑战性。在只有少量区域匹配的情况下，通过排除最佳匹配并显示结果与控制区域的第二最佳选择一致，使用综合控制方法进行稳健性检查。 10 合成控制方法放大了供体库的重要性，因为关于结果是否由随机误差引起的推断也取决于供体库的选择。这也许是综合控制方法中最新颖和最具挑战性的方面；由于存在差异中的差异，因此没有经过验证的参数方法来建立治疗和合成对照之间匹配的治疗后差异的置信区间。 Kakawa等人1采用普遍推荐的安慰剂测试方法，其中每个对照州都用于确定在没有治疗的情况下可以预期的治疗后结果水平的范围，因为对照州没有接受干预。虽然这里对来自引导程序等方法的模拟置信区间有一个粗略的类比，但合成控制方法的额外挑战是，估计效应参数的标准假设也是有效推断所估计的效应参数是否在安慰剂对照中观察到的自然变异范围内所必需的假设。也就是说，为了有效推断估计的精度，必须满足以下假设：（1）稳定的单位治疗值假设；（2）随机分配治疗的假设； (3) 假设区域的潜在结果是固定的但先验未知。 11 例如，想象一下纽约和马萨诸塞州与印第安纳州和田纳西州有很大不同（这与它们不作为最佳匹配控制区域的事实是一致的）。然后，这些区域被用来确定印第安纳州和田纳西州在没有发生干预的情况下预期的自然变化。废除全面背景调查没有影响的结论取决于从控制州样本中随机选择印第安纳州和田纳西州进行治疗的假设。当研究结论表明政策变化没有影响时，这是一个特殊的挑战。我们因此...
Epidemiologists, perhaps more than most disciplines given the field’s development of case-control methods, are acutely aware of the importance of appropriately selecting controls, and this is a critical decision to be made with the synthetic control method. The user selects the pool of eligible control regions. Kagawa et al1 appropriately select only eligible states, and because the treatment is repeal of a policy, they are limited to only the 8 other states that currently have the policy. This approach is most consistent with the selection of the control population in a case-control study with respect to the outcome of interest, selecting randomly from among those states at risk for the outcome. 7 Recent synthetic control method applications used as few as 168 but as many as 1309 regions in the donor pool. In addition, based on the matching shown in Table 2 of Kagawa et al, 1 half of the 8 states are not very good matches, with virtually all of the control states across outcomes made up of Illinois, Iowa, Michigan, and North Carolina. The primary advantage in the synthetic control is taking a data-driven approach to getting a close pretreatment match between treatment and control regions, but this advantage is more challenging to leverage when the sample of donor pool states is small. In scenarios where only a small number of regions match, robustness checks with the synthetic control method have been performed by excluding the best matches and showing that results were consistent with the second best selection of control regions. 10 The importance of the donor pool is magnified with the synthetic control method because inference about whether the results are due to random error also depends on the choice of the donor pool. This is perhaps the most novel and challenging aspect of the synthetic control method; there is not a validated, parametric approach to establishing confidence intervals on the matched posttreatment difference between the treatment and synthetic control as there is with difference in differences. Kagawa et al1 take the generally recommended approach of a placebo test where each of the control states is used to establish the range of the posttreatment level of the outcome that could be expected in the absence of treatment, since the control states did not receive the intervention. While there is a rough analogy here to simulated confidence intervals from approaches such as the bootstrap, the additional challenge for synthetic control method is that the standard assumptions for estimating an effect parameter are also required assumptions for valid inference on whether the effect parameter estimated is within the range of natural variation observed in the placebo controls. That is, the following assumptions must be met for valid inference about the precision of the estimates:(1) stable unit treatment value assumption;(2) assumption of random assignment to the treatment; and (3) assumption that the potential outcomes for regions are fixed but a priori unknown. 11 Imagine, for example, that New York and Massachusetts are regions that differ substantially from Indiana and Tennessee (which is consistent with the fact that they don’t serve as best matched control regions). These regions are then used to establish the natural variation that would be expected for Indiana and Tennessee if no intervention had occurred. The conclusion that there is no impact of the repeal of comprehensive background checks is dependent on the assumption that Indiana and Tennessee are randomly selected for treatment from the sample of control states. This is a particular challenge when study conclusions are that there was no impact of a policy change. We are thus …