Should age-period-cohort studies return to the methodologies of the 1970s?. (March 2015)

• Record Type: Journal Article
• Authors: Reither, Eric N.; Masters, Ryan K.; Yang, Yang Claire; Powers, Daniel A.; Zheng, Hui; Land, Kenneth C.
• Is Part Of: Social science & medicine. Volume 128(2015)
• Page Start: 356
• Page End: 365
• Publication Date: 2015-03
• Subjects: Age-period-cohort models -- Cohort effects -- Research methods -- Hierarchical modeling -- Random effects -- Body mass index -- Obesity epidemic -- Social change; Social medicine -- Periodicals; Medical anthropology -- Periodicals; Public health -- Periodicals; Psychology -- Periodicals; Medicine -- Periodicals; Medicine -- Periodicals; Médecine sociale -- Périodiques; Anthropologie médicale -- Périodiques; Santé publique -- Périodiques; Psychologie -- Périodiques; Médecine -- Périodiques; Electronic journals; 362.105
• DOI: 10.1016/j.socscimed.2015.01.011
• Languages: English
• ISSNs: 0277-9536

Abstract

Abstract: Social scientists have recognized the importance of age-period-cohort (APC) models for half a century, but have spent much of this time mired in debates about the feasibility of APC methods. Recently, a new class of APC methods based on modern statistical knowledge has emerged, offering potential solutions. In 2009, Reither, Hauser and Yang used one of these new methods – hierarchical APC (HAPC) modeling – to study how birth cohorts may have contributed to the U.S. obesity epidemic. They found that recent birth cohorts experience higher odds of obesity than their predecessors, but that ubiquitous period-based changes are primarily responsible for the rising prevalence of obesity. Although these findings have been replicated elsewhere, recent commentaries by Bell and Jones call them into question – along with the new class of APC methods. Specifically, Bell and Jones claim that new APC methods do not adequately address model identification and suggest that "solid theory" is often sufficient to remove one of the three temporal dimensions from empirical consideration. They also present a series of simulation models that purportedly show how the HAPC models estimated by Reither et al. (2009) could have produced misleading results. However, these simulation models rest on assumptions that there were no period effects, and associations between period and cohort variables and the outcome were perfectly linear. Those are conditions under which APC models should never be used. Under more tenable assumptions, our own simulations show that HAPC methods perform well, both in recovering the main findings presented by Reither et al. (2009) and the results reported by Bell and Jones. We also respond to critiques about model identification and theoretically-imposed constraints, finding little pragmatic support for such arguments. We conclude by encouraging social scientists to move beyond the debates of the 1970s and toward a deeper appreciation for modern APC methodologies. Highlights: We clarify the nature of the identification problem in conventional linear APC analysis. Cohorts and periods are social contexts; eliminating either via "solid theory" is often problematic. Like any statistical method, APC models fail when basic assumptions are violated. Hierarchical APC models perform well on data structures characteristic of real-world questions. Simulations are useful tools when they rest on tenable assumptions and plausible data structures.