Tarone R E, Chu K C
Biostatistics Branch, National Cancer Institute, Bethesda, MD 20892, USA.
Am J Epidemiol. 1996 Jan 1;143(1):85-91. doi: 10.1093/oxfordjournals.aje.a008661.
Interpretation of trends in disease rates using conventional age-period-cohort analyses is made difficult by the lack of a unique set of parameters specifying any given model. Because of difficulties inherent in age-period-cohort models, neither the magnitude nor the direction of a linear trend in birth cohort effects or calendar period effects can be determined unambiguously. This leads to considerable uncertainty in making inferences regarding disease etiology based on birth cohort or calendar period trends. In this paper, the authors demonstrate that changes in the direction or magnitude of long term trends can be identified unequivocally in age-period-cohort analyses, and they provide parametric methods for evaluating such changes in trend within the usual Poisson regression framework. Such changes can have important implications for disease etiology. This is demonstrated in applications of the proposed methods to the investigation of birth cohort trends in female breast cancer mortality rates obtained from the National Center for Health Statistics for the United States (1970-1989) and from the World Health Organization for Japan (1955-1979).
由于缺乏一组唯一的参数来指定任何给定模型，使用传统的年龄-时期-队列分析来解释疾病发病率趋势变得困难。由于年龄-时期-队列模型固有的困难，出生队列效应或日历时期效应的线性趋势的大小和方向都无法明确确定。这导致在基于出生队列或日历时期趋势推断疾病病因时存在相当大的不确定性。在本文中，作者证明了在年龄-时期-队列分析中可以明确识别长期趋势方向或大小的变化，并且他们提供了参数方法来在通常的泊松回归框架内评估这种趋势变化。这种变化可能对疾病病因具有重要意义。这在将所提出的方法应用于调查从美国国家卫生统计中心（1970 - 1989年）和世界卫生组织（1955 - 1979年）获得的日本女性乳腺癌死亡率的出生队列趋势中得到了证明。
