Roush G C, Schymura M J, Holford T R, White C, Flannery J T
J Natl Cancer Inst. 1985 Apr;74(4):779-88.
Out of necessity and convenience many reports on population-based rates for cancer are limited to analyses by time period of diagnosis, and just how often cohort effects are important in cancer data has not been fully explored. To address this question, Connecticut cancer incidence rates for the years 1940-79 were fitted to the model: Log (incidence rate) = constant + age effect + period effect + birth cohort effect + error term. Data for each cancer site and sex were categorized into 10-year intervals by time period and age group. Significance testing for the curvilinear effects (which are estimable functions) of age (A), period (P), and cohort (C) in the 44 data sets led to no clear choice of model for three data sets; an APC model for 20, an AP model for 7, and an AC model for 14. These choices were corroborated by the RA2 index. Limitations in the interpretation of the results were enumerated. Presentation of population-based cancer rates by implicitly assuming an AP model is valuable (e.g., for studying age distribution in different regions or for age-adjustment in examining international variation or time trends). However, the assumption of an AP model may often be incorrect, as was shown to be the case for most of these 44 data sets. The implications for monitoring trends and generating etiologic hypotheses were discussed in light of the results for cutaneous malignant melanoma and cancers of the cervix, breast, ovary, lung, and bladder.
出于必要性和便利性，许多基于人群的癌症发病率报告仅限于按诊断时间进行分析，而队列效应在癌症数据中究竟有多重要尚未得到充分探讨。为解决这个问题，将1940 - 1979年康涅狄格州的癌症发病率数据拟合到模型：Log（发病率）=常数+年龄效应+时期效应+出生队列效应+误差项。每个癌症部位和性别的数据按时间段和年龄组分为10年间隔。对44个数据集中年龄（A）、时期（P）和队列（C）的曲线效应（即可估计函数）进行显著性检验，结果显示有3个数据集无法明确选择模型；20个数据集选择APC模型，7个数据集选择AP模型，14个数据集选择AC模型。这些选择通过RA2指数得到了证实。文中列举了结果解释中的局限性。通过隐含假设AP模型来呈现基于人群的癌症发病率是有价值的（例如，用于研究不同地区的年龄分布或在研究国际差异或时间趋势时进行年龄调整）。然而，正如这44个数据集中的大多数情况所示，AP模型的假设往往可能是不正确的。根据皮肤恶性黑色素瘤以及子宫颈、乳腺、卵巢、肺和膀胱癌的研究结果，讨论了监测趋势和生成病因假设的意义。
