BACKGROUND Epidemiologists are often concerned with unobserved biases that produce confounding in population-based studies. We introduce a new design approach-'full matching incorporating an instrumental variable (IV)' or 'Full-IV Matching'-and illustrate its utility in reducing observed and unobserved biases to increase inference accuracy. Our motivating example is tailored to a central question in humanitarian emergencies-the difference in sexual violence risk by displacement setting. METHODS We conducted a series of 1000 Monte Carlo simulations generated from a population-based survey after the 2010 Haitian earthquake and included earthquake damage severity as an IV and the unmeasured variable of 'social capital'. We compared standardized mean differences (SMDs) for covariates after different designs to understand potential biases. Mean risk differences (RDs) were used to assess each design's accuracy in estimating the oracle of the simulated data set. RESULTS Naive analysis and pair matching equivalently performed. Full matching reduced imbalances between exposed and comparison groups across covariates, except for the unobserved covariate of 'social capital'. Pair and full matching overstated differences in sexual violence risk when displaced to a camp vs a community [pair: RD = 0.13, 95% simulation interval (SI) 0.09-0.16; full: RD = 0.11, 95% SI 0.08-0.14). Full-IV Matching reduced imbalances across observed covariates and importantly 'social capital'. The estimated risk difference (RD = 0.07, 95% SI 0.03-0.11) was closest to the oracle (RD = 0.06, 95% SI 0.4-0.8). CONCLUSION Full-IV Matching is a novel approach that is promising for increasing inference accuracy when unmeasured sources of bias likely exist.

中文翻译：

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预处理数据以减少偏差：在基于人群的研究中纳入工具变量的完全匹配。

背景 流行病学家经常关注在基于人群的研究中产生混杂的未观察到的偏差。我们介绍了一种新的设计方法——“包含工具变量 (IV) 的完全匹配”或“完全 IV 匹配”——并说明了它在减少观察到的和未观察到的偏差以提高推理准确性方面的效用。我们的激励性示例是针对人道主义紧急情况中的一个核心问题量身定制的——流离失所环境导致的性暴力风险差异。方法 我们对 2010 年海地地震后基于人口的调查进行了一系列 1000 次蒙特卡罗模拟，并将地震破坏严重程度作为 IV 和未测量变量“社会资本”包括在内。我们比较了不同设计后协变量的标准化均值差 (SMD)，以了解潜在的偏差。平均风险差 (RD) 用于评估每个设计在估计模拟数据集的 oracle 时的准确性。结果 朴素分析和对匹配等效执行。完全匹配减少了跨协变量的暴露组和比较组之间的不平衡，“社会资本”的未观察到的协变量除外。配对和完全匹配夸大了流离失所到营地与社区时性暴力风险的差异 [配对：RD = 0.13，95% 模拟区间 (SI) 0.09-0.16；全：RD = 0.11，95% SI 0.08-0.14）。全 IV 匹配减少了观察到的协变量和重要的“社会资本”之间的不平衡。估计的风险差异 (RD = 0.07, 95% SI 0.03-0. 11) 最接近神谕 (RD = 0.06, 95% SI 0.4-0.8)。结论 全 IV 匹配是一种新颖的方法，在可能存在未测量的偏差源时有望提高推理准确性。