Adjustment for time-invariant and time-varying confounders in 'unexplained residuals' models for longitudinal data within a causal framework and associated challenges.
Adjustment for time-invariant and time-varying confounders in 'unexplained residuals' models for longitudinal data within a causal framework and associated challenges.
复制标题
在因果关系框架和相关挑战中，调整了“无法解释的残差”模型中的时间不变和时变的混杂因素。
DOI：
10.1177/0962280218756158
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发表时间：
2019-05
影响因子：
2.3
通讯作者：
Gilthorpe MS
中科院分区：
文献类型：
--
作者：
Arnold KF;Ellison G;Gadd SC;Textor J;Tennant P;Heppenstall A;Gilthorpe MS
在生命周期流行病学中，“无法解释的残留物”模型被用来模拟在几个时间点纵向测量的暴露与远端结局的关系。有人声称，这些模型有几个优点，包括：能够在一个模型中估计多个总的因果效应，以及与传统回归方法相比，对暴露超过预期的增加对结果的影响有更多的洞察力。我们评估了这些性质，并从数学上证明了如何在这个模型框架内对混杂变量进行调整。重要的是，我们使用有向无环图将无法解释的残差模型显式地放置在因果框架中。这为适当的混杂因素调整提供了理论上的理由，并提供了一个框架，用于将我们的结果扩展到比本文所审查的情况更复杂的情况。我们还在因果框架内讨论了与未解释残差模型相关的几个解释问题。我们认为，与传统的回归方法相比，无法解释的残差模型并没有提供额外的见解，事实上，实施起来更具挑战性；此外，它们还人为地减少了估计的标准误差。因此，我们得出结论，如果使用无法解释的残差模型，必须非常小心地实施。
‘Unexplained residuals’ models have been used within lifecourse epidemiology to model an exposure measured longitudinally at several time points in relation to a distal outcome. It has been claimed that these models have several advantages, including: the ability to estimate multiple total causal effects in a single model, and additional insight into the effect on the outcome of greater-than-expected increases in the exposure compared to traditional regression methods. We evaluate these properties and prove mathematically how adjustment for confounding variables must be made within this modelling framework. Importantly, we explicitly place unexplained residual models in a causal framework using directed acyclic graphs. This allows for theoretical justification of appropriate confounder adjustment and provides a framework for extending our results to more complex scenarios than those examined in this paper. We also discuss several interpretational issues relating to unexplained residual models within a causal framework. We argue that unexplained residual models offer no additional insights compared to traditional regression methods, and, in fact, are more challenging to implement; moreover, they artificially reduce estimated standard errors. Consequently, we conclude that unexplained residual models, if used, must be implemented with great care.
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影响因子：
6.3
作者：
Menezes AM;Hallal PC;Dumith SC;Matijasevich AM;Araújo CL;Yudkin J;Osmond C;Barros FC;Victora CG
通讯作者：
Victora CG
影响因子：
4.9
作者：
Gonzalez, D. A.;Nazmi, A.;Victora, C. G.
通讯作者：
Victora, C. G.
影响因子：
7.7
作者：
Horta BL;Gigante DP;Osmond C;Barros FC;Victora CG
通讯作者：
Victora CG
影响因子：
168.9
作者：
Adair, Linda S.;Fall, Caroline H. D.;Osmond, Clive;Stein, Aryeh D.;Martorell, Reynaldo;Ramirez-Zea, Manuel;Sachdev, Harshpal Singh;Dahly, Darren L.;Bas, Isabelita;Norris, Shane A.;Micklesfield, Lisa;Hallal, Pedro;Victora, Cesar G.
通讯作者：
Victora, Cesar G.
影响因子：
16.2
作者：
Norris, Shane A.;Osmond, Clive;Fall, Caroline H. D.
通讯作者：
Fall, Caroline H. D.