From the aDepartment of Epidemiology, bDepartment of Biostatistics and Bioinformatics, and cDepartment of Environmental and Occupational Health, Rollins School of Public Health, Emory University, Atlanta, GA.
Epidemiology. 2015 Jul;26(4):481-9. doi: 10.1097/EDE.0000000000000286.
Population causal effects are often defined as contrasts of average individual-level counterfactual outcomes, comparing different exposure levels. Common examples include causal risk difference and risk ratios. These and most other examples emphasize effects on disease onset, a reflection of the usual epidemiological interest in disease occurrence. Exposure effects on other health characteristics, such as prevalence or conditional risk of a particular disability, can be important as well, but contrasts involving these other measures may often be dismissed as non-causal. For example, an observed prevalence ratio might often viewed as an estimator of a causal incidence ratio and hence subject to bias. In this manuscript, we provide and evaluate a definition of causal effects that generalizes those previously available. A key part of the generalization is that contrasts used in the definition can involve multivariate, counterfactual outcomes, rather than only univariate outcomes. An important consequence of our generalization is that, using it, one can properly define causal effects based on a wide variety of additional measures. Examples include causal prevalence ratios and differences and causal conditional risk ratios and differences. We illustrate how these additional measures can be useful, natural, easily estimated, and of public health importance. Furthermore, we discuss conditions for valid estimation of each type of causal effect, and how improper interpretation or inferences for the wrong target population can be sources of bias.
人群因果效应通常被定义为比较不同暴露水平的平均个体水平反事实结果的对比。常见的例子包括因果风险差异和风险比。这些以及大多数其他例子都强调了对疾病发病的影响，反映了流行病学对疾病发生的通常兴趣。暴露对其他健康特征的影响，如特定残疾的患病率或条件风险，也可能同样重要，但涉及这些其他措施的对比可能经常被视为非因果关系。例如，观察到的患病率比通常被视为因果发病率比的估计值，因此容易产生偏差。在本文中，我们提供并评估了一种因果效应的定义，该定义扩展了以前可用的定义。该扩展的一个关键部分是定义中使用的对比可以涉及多元、反事实的结果，而不仅仅是单变量结果。我们的推广的一个重要结果是，使用它，可以根据广泛的其他措施正确定义因果效应。示例包括因果患病率比和差异以及因果条件风险比和差异。我们说明了这些额外措施如何有用、自然、易于估计，以及对公共卫生的重要性。此外，我们讨论了每种类型因果效应的有效估计的条件，以及错误的目标人群的不正确解释或推断如何成为偏差的来源。
