At-risk-measure Sampling in Case-Control Studies with Aggregated Data.
At-risk-measure Sampling in Case-Control Studies with Aggregated Data.
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DOI：
10.1097/ede.0000000000001268
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发表时间：
2021-01
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影响因子：
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通讯作者：
Flanders WD
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文献类型：
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作者：
Garber MD;McCullough LE;Mooney SJ;Kramer MR;Watkins KE;Lobelo RLF;Flanders WD
补充数字内容可在正文中找到。在流行病学研究中，瞬时暴露很难测量，特别是当结果的风险状态和暴露随时间和空间变化时，例如在测量交通伤害的建筑环境风险时。由移动传感器产生的当代“大数据”可以改进瞬时曝光的测量。这些设备产生的暴露信息通常只采样目标队列的经验，因此病例对照框架可能是有用的。然而，为了匿名，个人可能无法获得数据，这排除了病例交叉方法。我们提出了一种称为风险度量抽样的方法。它的目标是估计发病率比的分母(暴露于未暴露的风险体验测量)，给定来自队列的风险测量的汇总。该方法不是对个人或地点进行抽样，而是对风险体验的衡量进行抽样。具体地说，提出的方法对按地点概括的人-距离和人-事件进行采样。它以一款用于记录骑自行车的移动应用程序的数据为例。该方法扩展了已建立的病例对照抽样原则：对队列研究的风险经验进行抽样，以使抽样的暴露分布接近于队列的暴露分布。它与密度抽样的不同之处在于，样本仍然以风险测量的形式存在，这种测量可以是连续的，例如人-时间或人-距离。如果这样的样本已经可用，例如来自诸如聚合的移动传感器数据之类的大数据来源，则这方面可能在逻辑和统计上都是有效的。
Supplemental Digital Content is available in the text. Transient exposures are difficult to measure in epidemiologic studies, especially when both the status of being at risk for an outcome and the exposure change over time and space, as when measuring built-environment risk on transportation injury. Contemporary “big data” generated by mobile sensors can improve measurement of transient exposures. Exposure information generated by these devices typically only samples the experience of the target cohort, so a case-control framework may be useful. However, for anonymity, the data may not be available by individual, precluding a case–crossover approach. We present a method called at-risk-measure sampling. Its goal is to estimate the denominator of an incidence rate ratio (exposed to unexposed measure of the at-risk experience) given an aggregated summary of the at-risk measure from a cohort. Rather than sampling individuals or locations, the method samples the measure of the at-risk experience. Specifically, the method as presented samples person–distance and person–events summarized by location. It is illustrated with data from a mobile app used to record bicycling. The method extends an established case–control sampling principle: sample the at-risk experience of a cohort study such that the sampled exposure distribution approximates that of the cohort. It is distinct from density sampling in that the sample remains in the form of the at-risk measure, which may be continuous, such as person–time or person–distance. This aspect may be both logistically and statistically efficient if such a sample is already available, for example from big-data sources like aggregated mobile-sensor data.