Clarifying hierarchical age-period-cohort models: A rejoinder to Bell and Jones
Clarifying hierarchical age-period-cohort models: A rejoinder to Bell and Jones
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DOI：
10.1016/j.socscimed.2015.07.013
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发表时间：
2015-11-01
影响因子：
5.4
通讯作者：
Yang, Y. Claire
中科院分区：
文献类型：
--
作者：
Reither, Eric N.;Land, Kenneth C.;Yang, Y. Claire
以前，Rheither等人（2015）证明，当基本假设得到满足时，分层年龄阶段队列（HAPC）模型表现良好。为了反驳这一发现，Bell和Jones（2015）发明了一种数据生成过程（DGP），该过程从Rheither等人（2015）的不同方程中借用了年龄，时期和队列效应。当HAPC模型应用于DGP模拟的数据时，未能恢复APC效应的模式，B&J重申他们的观点，即这些模型提供了“伪装成科学的误导性证据”。“尽管措辞强硬，但B&J对他们自己的模拟数据并不好奇，因此再次将HAPC模型错误地应用于违反重要假设的数据。在这个回答中，我们说明了一个谨慎的分析师如何使用简单的描述性图和模型选择统计来验证（a）这些数据中不存在周期效应，（B）年龄和队列效应被合并。通过考虑B&J的人工数据结构的特点，我们成功地恢复了“真正的”DGP通过适当指定的模型。我们得出结论，B& J对科学的主要贡献是提醒分析师，APC模型将在存在精确代数效应的情况下失败（即，没有随机/随机分量的效应），以及当包括共线时间维度而在建模过程中不特别注意时。这篇评论的共同作者的扩展列表代表了APC学者之间正在形成的共识，即B&J的基本策略-用违反重要原则的人为DGP模拟的数据测试HAPC模型-并不是推进流行病学和社会科学中关于创新APC方法的讨论的有效方法。(C)2015爱思唯尔有限公司版权所有。
Previously, Reither et al. (2015) demonstrated that hierarchical age-period-cohort (HAPC) models perform well when basic assumptions are satisfied. To contest this finding, Bell and Jones (2015) invent a data generating process (DGP) that borrows age, period and cohort effects from different equations in Reither et al. (2015). When HAPC models applied to data simulated from this DGP fail to recover the patterning of APC effects, B&J reiterate their view that these models provide "misleading evidence dressed up as science." Despite such strong words, B&J show no curiosity about their own simulated data and therefore once again misapply HAPC models to data that violate important assumptions. In this response, we illustrate how a careful analyst could have used simple descriptive plots and model selection statistics to verify that (a) period effects are not present in these data, and (b) age and cohort effects are conflated. By accounting for the characteristics of B&J's artificial data structure, we successfully recover the "true" DGP through an appropriately specified model. We conclude that B&Js main contribution to science is to remind analysts that APC models will fail in the presence of exact algebraic effects (i.e., effects with no random/stochastic components), and when collinear temporal dimensions are included without taking special care in the modeling process. The expanded list of coauthors on this commentary represents an emerging consensus among APC scholars that B&J's essential strategy-testing HAPC models with data simulated from contrived DGPs that violate important assumptions-is not a productive way to advance the discussion about innovative APC methods in epidemiology and the social sciences. (C) 2015 Elsevier Ltd. All rights reserved.