论文标题
几何平均值在病例对照研究中的作用
The role of the geometric mean in case-control studies
论文作者
论文摘要
历史上用于罕见结果或数据收集昂贵的设置中，与结果相关的采样与许多现代设置有关，在许多现代设置中，数据可容易用于目标人群的有偏见样本（例如公共行政数据）。在依赖结果的采样下，未确定诸如平均风险差异和平均风险比率之类的常见效应措施，但条件上的优势比为。条件优势比的聚合具有挑战性，因为通常未确定汇总措施。此外，边际优势比可以大于所有条件优势比。如果我们使用标准算术平均值的替代汇总，则可以避免这种所谓的优势比的非碰撞能力。我们提供了可折叠性的新定义，该定义使这种聚合方法的选择显式，并证明了几何聚集的几率比值可折叠。我们描述了如何部分识别，估计和推断在结果依赖性采样下的几何比值比。我们提出的估计器基于有效的影响函数，因此具有双重稳健式的性能。
Historically used in settings where the outcome is rare or data collection is expensive, outcome-dependent sampling is relevant to many modern settings where data is readily available for a biased sample of the target population, such as public administrative data. Under outcome-dependent sampling, common effect measures such as the average risk difference and the average risk ratio are not identified, but the conditional odds ratio is. Aggregation of the conditional odds ratio is challenging since summary measures are generally not identified. Furthermore, the marginal odds ratio can be larger (or smaller) than all conditional odds ratios. This so-called non-collapsibility of the odds ratio is avoidable if we use an alternative aggregation to the standard arithmetic mean. We provide a new definition of collapsibility that makes this choice of aggregation method explicit, and we demonstrate that the odds ratio is collapsible under geometric aggregation. We describe how to partially identify, estimate, and do inference on the geometric odds ratio under outcome-dependent sampling. Our proposed estimator is based on the efficient influence function and therefore has doubly robust-style properties.
