Should age-period-cohort analysts accept innovation without scrutiny? A response to Reither, Masters, Yang, Powers, Zheng and Land

• Published in: Social science & medicine (1982) 2015-03, Vol.128, p.331-333
• Main Authors: Bell, Andrew, Jones, Kelvyn
• Format: Article
• Language: English
• Subjects: Age-period-cohort models; Cohort Effect; Cohort effects; Collinearity; Female; Health Status Disparities; Humans; Innovations; Male; Mathematical functions; Model identification; Multilevel modelling; Obesity; Obesity - epidemiology; Theory
• ISSN: 0277-9536; 1873-5347
• DOI: 10.1016/j.socscimed.2015.01.040

This commentary clarifies our original commentary (Bell and Jones, 2014c) and illustrates some concerns we have regarding the response article in this issue (Reither et al., 2015). In particular, we argue that (a) linear effects do not have to be produced by exact linear mathematical functions to behave as if they were linear, (b) linear effects by this wider definition are extremely common in real life social processes, and (c) in the presence of these effects, the Hierarchical Age Period Cohort (HAPC) model will often not work. Although Reither et al. do not define what a ‘non-linear monotonic trend’ is (instead, only stating that it isn't a linear effect) we show that the model often doesn't work in the presence of such effects, by using data generated as a ‘non-linear monotonic trend’ by Reither et al. themselves. We then question their discussion of fixed and random effects before finishing with a discussion of how we argue that theory should be used, in the context of the obesity epidemic. •We clarify the nature of the identification problem in all APC analysis.•The Hierarchical APC model will sometimes work, but sometimes is not enough.•Simulations using plausible data structures show the model often does not work.•Relying in theory is problematic, but this is often all researchers can do.