Epidemiol Rev. 2022 Jan 14;43(1):106-117. doi: 10.1093/epirev/mxab012.
Quantitative bias analysis can be used to empirically assess how far study estimates are from the truth (i.e., an estimate that is free of bias). These methods can be used to explore the potential impact of confounding bias, selection bias (collider stratification bias), and information bias. Quantitative bias analysis includes methods that can be used to check the robustness of study findings to multiple types of bias and methods that use simulation studies to generate data and understand the hypothetical impact of specific types of bias in a simulated data set. In this article, we review 2 strategies for quantitative bias analysis: 1) traditional probabilistic quantitative bias analysis and 2) quantitative bias analysis with generated data. An important difference between the 2 strategies relates to the type of data (real vs. generated data) used in the analysis. Monte Carlo simulations are used in both approaches, but the simulation process is used for different purposes in each. For both approaches, we outline and describe the steps required to carry out the quantitative bias analysis and also present a bias-analysis tutorial demonstrating how both approaches can be applied in the context of an analysis for selection bias. Our goal is to highlight the utility of quantitative bias analysis for practicing epidemiologists and increase the use of these methods in the epidemiologic literature.
定量偏倚分析可用于实证评估研究估计值与真实值（即无偏估计值）之间的差距。这些方法可用于探索混杂偏倚、选择偏倚（混杂分层偏倚）和信息偏倚的潜在影响。定量偏倚分析包括可用于检查研究结果对多种偏倚稳健性的方法，以及使用模拟研究生成数据并了解模拟数据集中特定类型偏倚假设影响的方法。本文综述了 2 种定量偏倚分析策略：1）传统概率定量偏倚分析和 2）基于生成数据的定量偏倚分析。这 2 种策略的一个重要区别在于分析中使用的数据类型（真实数据与生成数据）。这两种方法都使用了蒙特卡罗模拟，但在每种方法中，模拟过程的用途都不同。对于这两种方法，我们都概述并描述了进行定量偏倚分析所需的步骤，并提供了一个偏倚分析教程，演示了如何在选择偏倚分析的背景下应用这两种方法。我们的目标是强调定量偏倚分析对实践流行病学的实用性，并增加这些方法在流行病学文献中的应用。
