Are all biases missing data problems?
Are all biases missing data problems?
复制标题
所有偏见都存在缺失数据的问题吗？
DOI：
10.1007/s40471-015-0050-8
复制
发表时间：
2015
影响因子：
3.3
通讯作者：
Hogan,JosephW
中科院分区：
文献类型：
--
作者：
Howe,ChanelleJ;Cain,LaurenE;Hogan,JosephW
估计因果效应是流行病学研究的一个常见目标。传统上，对因果影响的一致估计存在三个既定的系统性威胁。这三个威胁是由于混杂因素、选择和测量误差造成的偏差。混淆、选择和测量偏差通常被描述为不同类型的偏差。然而，这些偏差中的每一个也可以被描述为丢失数据问题，这些问题可以通过丢失数据解决方案来解决。在这里，我们描述了上述系统性威胁是如何因数据缺失而产生的，以及用于减少每种偏差类型的审查方法及其相关假设。我们还将回顾的方法所做的假设与缺失数据框架中所做的完全随机缺失(MCAR)和随机缺失(MAR)假设联系起来，这些假设允许基于观察到的不完整数据做出有效的推断。
Estimating causal effects is a frequent goal of epidemiologic studies. Traditionally, there have been three established systematic threats to consistent estimation of causal effects. These three threats are bias due to confounders, selection, and measurement error. Confounding, selection, and measurement bias have typically been characterized as distinct types of biases. However, each of these biases can also be characterized as missing data problems that can be addressed with missing data solutions. Here we describe how the aforementioned systematic threats arise from missing data as well as review methods and their related assumptions for reducing each bias type. We also link the assumptions made by the reviewed methods to the missing completely at random (MCAR) and missing at random (MAR) assumptions made in the missing data framework that allow for valid inferences to be made based on the observed, incomplete data.