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.
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.
Soc Sci Med. 2014 Jan;101:176-80. doi: 10.1016/j.socscimed.2013.09.004. Epub 2013 Sep 19.
Reither, Hauser, and Yang (2009) use a Hierarchical Age-Period-Cohort model (HAPC - Yang & Land, 2006) to assess changes in obesity in the USA population. Their results suggest that there is only a minimal effect of cohorts, and that it is periods which have driven the increase in obesity over time. We use simulations to show that this result may be incorrect. Using simulated data in which it is cohorts, rather than periods, that are responsible for the rise in obesity, we are able to replicate the period-trending results of Reither et al. In this instance, the HAPC model misses the true cohort trend entirely, erroneously finds a period trend, and underestimates the age trend. Reither et al.'s results may be correct, but because age, period and cohort are confounded there is no way to tell. This is typical of age-period-cohort models, and shows the importance of caution when any APC model is used. We finish with a discussion of ways forward for researchers wishing to model age, period and cohort in a robust and non-arbitrary manner.
赖瑟、豪泽和杨（2009 年）使用分层年龄-时期-队列模型（HAPC-杨和兰德，2006 年）来评估美国人口中肥胖的变化。他们的结果表明，队列的影响微乎其微，是时期导致了肥胖的持续增加。我们通过模拟表明，这一结果可能是不正确的。我们使用模拟数据，其中是队列而不是时期导致肥胖的增加，我们能够复制赖瑟等人的时期趋势结果。在这种情况下，HAPC 模型完全忽略了真正的队列趋势，错误地发现了一个时期趋势，并低估了年龄趋势。赖瑟等人的结果可能是正确的，但由于年龄、时期和队列相互混淆，因此无法判断。这是年龄-时期-队列模型的典型特征，表明在使用任何 APC 模型时都要谨慎。最后，我们讨论了希望以稳健和非任意方式对年龄、时期和队列进行建模的研究人员的前进方向。
