An Assessment and Extension of the Mechanism-Based Approach to the Identification of Age-Period-Cohort Models.
An Assessment and Extension of the Mechanism-Based Approach to the Identification of Age-Period-Cohort Models.
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DOI：
10.1007/s13524-017-0562-6
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发表时间：
2017-04
期刊：
影响因子：
3.5
通讯作者：
De Stavola BL
中科院分区：
文献类型：
--
作者：
Bijlsma MJ;Daniel RM;Janssen F;De Stavola BL
已经提出了许多方法来解决年龄-时期-队列（APC）的线性识别问题，但大多数没有理论上的信息，并可能导致有偏估计的APC效果。一个例外是最近提出的机制为基础的方法，并根据珍珠的前门标准，这种方法确保一致的APC效应估计在一个完整的中间变量之间的年龄，时期，队列，和利益的结果，只要假设的参数模型的所有相关的因果途径是正确的。通过模拟APC关于心血管死亡率的数据的模拟研究，我们证明了基于机制的方法的用户在现实条件下可能遇到的陷阱：即，当（1）可用中间变量的集合不完整，（2）中间变量受两个或更多APC变量的影响时（虽然分析中没有承认这一特征），以及（3）中间变量和结果之间存在无法解释的混淆。此外，我们展示了如何基于机制的方法可以扩展到原来提出的线性和概率回归模型，将所有广义线性模型，以及非线性的预测，使用蒙特卡罗模拟。基于观察到的偏差所造成的偏离基本假设，我们制定的应用指南的机制为基础的方法（扩展或不）。本文的在线版本（doi：10.1007/s13524-017-0562-6）包含补充材料，可供授权用户使用。
Many methods have been proposed to solve the age-period-cohort (APC) linear identification problem, but most are not theoretically informed and may lead to biased estimators of APC effects. One exception is the mechanism-based approach recently proposed and based on Pearl’s front-door criterion; this approach ensures consistent APC effect estimators in the presence of a complete set of intermediate variables between one of age, period, cohort, and the outcome of interest, as long as the assumed parametric models for all the relevant causal pathways are correct. Through a simulation study mimicking APC data on cardiovascular mortality, we demonstrate possible pitfalls that users of the mechanism-based approach may encounter under realistic conditions: namely, when (1) the set of available intermediate variables is incomplete, (2) intermediate variables are affected by two or more of the APC variables (while this feature is not acknowledged in the analysis), and (3) unaccounted confounding is present between intermediate variables and the outcome. Furthermore, we show how the mechanism-based approach can be extended beyond the originally proposed linear and probit regression models to incorporate all generalized linear models, as well as nonlinearities in the predictors, using Monte Carlo simulation. Based on the observed biases resulting from departures from underlying assumptions, we formulate guidelines for the application of the mechanism-based approach (extended or not). The online version of this article (doi:10.1007/s13524-017-0562-6) contains supplementary material, which is available to authorized users.