Many causal models of interest in epidemiology involve longitudinal exposures, confounders and mediators. However, repeated measurements are not always available or used in practice, leading analysts to overlook the time-varying nature of exposures and work under over-simplified causal models. Our objective is to assess whether - and how - causal effects identified under such misspecified causal models relates to true causal effects of interest. We derive sufficient conditions ensuring that the quantities estimated in practice under over-simplified causal models can be expressed as weighted averages of longitudinal causal effects of interest. Unsurprisingly, these sufficient conditions are very restrictive, and our results state that the quantities estimated in practice should be interpreted with caution in general, as they usually do not relate to any longitudinal causal effect of interest. Our simulations further illustrate that the bias between the quantities estimated in practice and the weighted averages of longitudinal causal effects of interest can be substantial. Overall, our results confirm the need for repeated measurements to conduct proper analyses and/or the development of sensitivity analyses when they are not available.

翻译：流行病学中许多感兴趣的因果模型涉及纵向暴露、混杂因素和中介变量。然而，重复测量在现实中并非总是可用或被采用，导致分析者忽视暴露的时间动态性质，并在过度简化的因果模型下开展分析。本文旨在评估此类误设因果模型所识别的因果效应是否——以及如何——与真正感兴趣的纵向因果效应相关。我们推导出充分条件，确保过度简化因果模型下实践中估计的量可表示为感兴趣纵向因果效应的加权平均。不出所料，这些充分条件非常严格，我们的结果表明，实践中估计的量通常应谨慎解读，因为它们通常与任何感兴趣的纵向因果效应无关。模拟进一步表明，实践中估计的量与感兴趣纵向因果效应的加权平均之间的偏差可能相当大。总体而言，我们的结果证实了开展恰当分析需要重复测量，或在缺乏重复测量时需开发敏感性分析方法。