File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Clarifying hierarchical age-period-cohort models: A rejoinder to Bell and Jones

TitleClarifying hierarchical age-period-cohort models: A rejoinder to Bell and Jones
Authors
KeywordsAge-period-cohort models
Body mass index
Cohort effects
Hierarchical modeling
Obesity epidemic
Random effects
Simulation models
Social change
Issue Date2015
Citation
Social Science and Medicine, 2015, v. 145, p. 125-128 How to Cite?
AbstractPreviously, 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.
Persistent Identifierhttp://hdl.handle.net/10722/334408
ISSN
2023 Impact Factor: 4.9
2023 SCImago Journal Rankings: 1.954
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorReither, Eric N.-
dc.contributor.authorLand, Kenneth C.-
dc.contributor.authorJeon, Sun Y.-
dc.contributor.authorPowers, Daniel A.-
dc.contributor.authorMasters, Ryan K.-
dc.contributor.authorZheng, Hui-
dc.contributor.authorHardy, Melissa A.-
dc.contributor.authorKeyes, Katherine M.-
dc.contributor.authorFu, Qiang-
dc.contributor.authorHanson, Heidi A.-
dc.contributor.authorSmith, Ken R.-
dc.contributor.authorUtz, Rebecca L.-
dc.contributor.authorClaire Yang, Y.-
dc.date.accessioned2023-10-20T06:47:55Z-
dc.date.available2023-10-20T06:47:55Z-
dc.date.issued2015-
dc.identifier.citationSocial Science and Medicine, 2015, v. 145, p. 125-128-
dc.identifier.issn0277-9536-
dc.identifier.urihttp://hdl.handle.net/10722/334408-
dc.description.abstractPreviously, 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.-
dc.languageeng-
dc.relation.ispartofSocial Science and Medicine-
dc.subjectAge-period-cohort models-
dc.subjectBody mass index-
dc.subjectCohort effects-
dc.subjectHierarchical modeling-
dc.subjectObesity epidemic-
dc.subjectRandom effects-
dc.subjectSimulation models-
dc.subjectSocial change-
dc.titleClarifying hierarchical age-period-cohort models: A rejoinder to Bell and Jones-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.socscimed.2015.07.013-
dc.identifier.pmid26277370-
dc.identifier.scopuseid_2-s2.0-84946480909-
dc.identifier.volume145-
dc.identifier.spage125-
dc.identifier.epage128-
dc.identifier.eissn1873-5347-
dc.identifier.isiWOS:000365060300015-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats