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A further critique of the analytic strategy of adjusting for covariates to identify biologic mediation

机译：对调整协变量以识别生物中介的分析策略的进一步批评

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Background Epidemiologic research is often devoted to etiologic investigation, and so techniques that may facilitate mechanistic inferences are attractive. Some of these techniques rely on rigid and/or unrealistic assumptions, making the biologic inferences tenuous. The methodology investigated here is effect decomposition : the contrast between effect measures estimated with and without adjustment for one or more variables hypothesized to lie on the pathway through which the exposure exerts its effect. This contrast is typically used to distinguish the exposure's indirect effect, through the specified intermediate variables, from its direct effect, transmitted via pathways that do not involve the specified intermediates. Methods We apply a causal framework based on latent potential response types to describe the limitations inherent in effect decomposition analysis. For simplicity, we assume three measured binary variables with monotonic effects and randomized exposure, and use difference contrasts as measures of causal effect. Previous authors showed that confounding between intermediate and the outcome threatens the validity of the decomposition strategy, even if exposure is randomized. We define exchangeability conditions for absence of confounding of causal effects of exposure and intermediate, and generate two example populations in which the no-confounding conditions are satisfied. In one population we impose an additional prohibition against unit-level interaction (synergism). We evaluate the performance of the decomposition strategy against true values of the causal effects, as defined by the proportions of latent potential response types in the two populations. Results We demonstrate that even when there is no confounding, partition of the total effect into direct and indirect effects is not reliably valid. Decomposition is valid only with the additional restriction that the population contain no units in which exposure and intermediate interact to cause the outcome. This restriction implies homogeneity of causal effects across strata of the intermediate. Conclusions Reliable effect decomposition requires not only absence of confounding, but also absence of unit-level interaction and use of linear contrasts as measures of causal effect. Epidemiologists should be wary of etiologic inference based on adjusting for intermediates, especially when using ratio effect measures or when absence of interacting potential response types cannot be confidently asserted.
机译：背景技术流行病学研究通常致力于病因学研究，因此可以促进机理推断的技术很有吸引力。这些技术中的某些依赖于僵化和/或不切实际的假设，从而使生物学推断变得微不足道。此处研究的方法是效应分解：在对一个或多个变量进行调整和不进行调整的情况下，评估和不评估调整的效应措施之间的对比，假设该变量位于暴露发挥作用的途径上。这种对比通常用于通过指定的中间变量将暴露的间接作用与通过不涉及指定中间物的途径传递的直接作用相区别。方法我们基于潜在的潜在响应类型应用因果框架来描述效应分解分析中固有的局限性。为简单起见，我们假设三个测得的具有单调效应和随机暴露的二元变量，并使用差异对比作为因果效应的量度。先前的作者表明，即使暴露是随机的，中间结果与结果之间的混淆也威胁到分解策略的有效性。我们定义了不存在暴露和中间因果关系混淆的可交换性条件，并生成了两个满足无混淆条件的示例种群。在一个人群中，我们对单位级别的互动（协同作用）施加了另一种禁止。我们根据因果效应的真实值评估分解策略的性能，因果效应由两个总体中潜在潜在响应类型的比例定义。结果我们证明，即使没有混淆，将总效果分为直接和间接效果也不可靠。分解仅在人口不包含暴露和中间体相互作用导致结果的单位的附加限制下才有效。这种限制意味着整个中间层的因果效应是同质的。结论可靠的效果分解不仅需要避免混淆，而且还需要单元级的交互作用以及使用线性对比作为因果关系的量度。流行病学家应警惕根据中间体的调整进行病因推断，尤其是在使用比率效应测度或无法确定缺乏相互作用的潜在反应类型时。

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