Lee W C, Lin R S
Institute of Epidemiology, College of Public Health, National Taiwan University, Taipei, Republic of China.
Int J Epidemiol. 1995 Aug;24(4):671-7. doi: 10.1093/ije/24.4.671.
Recently the age-period-cohort (APC) model has become a popular epidemiological tool. However, it is well known that the model suffers from the identifiability problem. The simple multiplicative formulation of the model in terms of the age, period, and cohort variables without resorting to the underlying biology also casts doubt on the interpretability of the model parameters.
Excess risk APC models for cancers are developed based on carcinogenesis processes in human populations. These models have the beneficial feature of biological plausibility and do not suffer from the identifiability problem. Apart from the age, period, and cohort effects, a new kind of effect, the impact effect, is also introduced into the models. A computer program has been developed to fit the models which contain non-linear as well as restricted parameters.
Two published mortality datasets are used to demonstrate the methodology. The proposed models fit better than the conventional APC model in both examples.
Despite all the merits of the proposed models, several statistical issues should be investigated further before accepting this methodology as a general data-analytical tool.
最近，年龄-时期-队列（APC）模型已成为一种流行的流行病学工具。然而，众所周知，该模型存在可识别性问题。该模型在年龄、时期和队列变量方面采用简单的乘法公式，而不考虑潜在生物学因素，这也让人对模型参数的可解释性产生怀疑。
基于人群中的致癌过程，开发了癌症超额风险APC模型。这些模型具有生物学合理性这一有益特征，并且不存在可识别性问题。除了年龄、时期和队列效应外，模型中还引入了一种新的效应，即影响效应。已开发出一个计算机程序来拟合包含非线性参数和受限参数的模型。
使用两个已发表的死亡率数据集来演示该方法。在两个例子中，所提出的模型都比传统的APC模型拟合得更好。
尽管所提出的模型有诸多优点，但在将该方法作为一种通用的数据分析工具接受之前，仍有几个统计问题需要进一步研究。
