A General, Multivariate Definition of Causal Effects in Epidemiology
A General, Multivariate Definition of Causal Effects in Epidemiology
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DOI：
10.1097/ede.0000000000000286
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发表时间：
2015-07-01
期刊：
影响因子：
5.4
通讯作者：
Klein, Mitchel
中科院分区：
文献类型：
--
作者：
Flanders, W. Dana;Klein, Mitchel
人群因果效应通常被定义为平均个人水平的反事实结果的对比，比较不同的暴露水平。常见的例子包括因果风险差异和风险比率。这些例子和大多数其他例子都强调对疾病发病的影响，这反映了疾病发生的通常流行病学兴趣。暴露对其他健康特征的影响，如某一特定残疾的患病率或条件风险，也可能很重要，但涉及这些其他措施的对比往往可能被认为是无关紧要的。例如，观察到的患病率可能通常被视为因果发病率的估计值，因此容易产生偏差。在这份手稿中，我们提供并评估了对因果效应的定义，该定义概括了那些先前可用的定义。概括的一个关键部分是，定义中使用的对比可以涉及多变量的、反事实的结果，而不仅仅是单变量的结果。我们推广的一个重要结果是，使用它，人们可以根据各种额外的衡量标准适当地定义因果关系。例子包括因果流行比率和差异以及因果条件风险比率和差异。我们将说明这些额外措施是如何有用的、自然的、易于估计的以及对公共卫生的重要性。此外，我们讨论了每种类型的因果效应的有效估计的条件，以及对错误的目标人群的不适当的解释或推断如何可能成为偏见的来源。
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.