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A comparison of alternative strategies for choosing control populations in observational studies

机译：观察研究中选择对照人群的替代策略比较

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Various approaches have been used to select control groups in observational studies: (1) from within the intervention area; (2) from a convenience sample, or randomly chosen areas; (3) from areas matched on area-level characteristics; and (4) nationally. The consequences of the decision are rarely assessed but, as we show, it can have complex impacts on confounding at both the area and individual levels. We began by reanalyzing data collected for an evaluation of a rapid response service on rates of unplanned hospital admission. Balance on observed individual-level variables was better with external than local controls, after matching. Further, when important prognostic variables were omitted from the matching algorithm, imbalances on those variables were also minimized using external controls. Treatment effects varied markedly depending on the choice of control area, but in the case study the variation was minimal after adjusting for the characteristics of areas. We used simulations to assess relative bias and means-squared error, as this could not be done in the case study. A particular feature of the simulations was unexplained variation in the outcome between areas. We found that the likely impact of unexplained variation for hospital admissions dwarfed the benefits of better balance on individual-level variables, leading us to prefer local controls in this instance.
机译：在观察研究中，已经采用了各种方法来选择对照组：（1）从干预区域内进行; （2）从便利样本或随机选择的区域中获取; （3）来自符合地区特征的地区; （4）在全国范围内。决定的后果很少评估，但是，正如我们所展示的那样，它可能对区域和个人层面上的混淆产生复杂的影响。我们首先重新分析收集的数据，以评估计划外医院入院率的快速响应服务。匹配后，外部控制的局部个体变量的平衡优于局部控制。此外，当匹配算法中省略了重要的预后变量时，也可以使用外部控件将这些变量的不平衡最小化。根据控制区域的选择，治疗效果显着不同，但在本案例研究中，在调整区域特征后，变化很小。我们使用模拟来评估相对偏差和均方误差，因为在案例研究中无法做到这一点。模拟的一个特殊特征是区域之间结果的无法解释的变化。我们发现，无法解释的变化对住院人数的可能影响使更好地平衡个人水平变量的好处相形见,，导致我们在这种情况下更喜欢使用本地控制。

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