title: Should age-period-cohort analysts accept innovation without scrutiny? A response to Reither, Masters, Yang, Powers, Zheng and Land.
abstract: 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.
authors: School of Geographical Sciences, University of Bristol, University Road, Bristol, BS8 1SS, UK; Centre for Multilevel Modelling, University of Bristol, 2 Priory Road, Bristol, BS8 1TX, UK.
authors: Andrew
authors: A
authors: Bell
authors: School of Geographical Sciences, University of Bristol, University Road, Bristol, BS8 1SS, UK; Centre for Multilevel Modelling, University of Bristol, 2 Priory Road, Bristol, BS8 1TX, UK.
authors: Kelvyn
authors: K
authors: Jones
mesh_terms: D015988:Cohort Effect; D005260:Female; D054624:Health Status Disparities; D006801:Humans; D008297:Male; D009765:Obesity / Q000453:epidemiology*
keywords: Age-period-cohort models; Cohort effects; Collinearity; Model identification; Multilevel modelling; Obesity