https://scholars.lib.ntu.edu.tw/handle/123456789/521749
標題: | Analysis of cancer rates using excess risk age-period-cohort models | 作者: | WEN-CHUNG LEE Lin R.S. |
公開日期: | 1995 | 卷: | 24 | 期: | 4 | 起(迄)頁: | 671-677 | 來源出版物: | International Journal of Epidemiology | 摘要: | Background: 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. Methods: Excess risk APC models for cancers are developed based on carclnogenesls 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. Results: Two published mortality datasets are used to demonstrate the methodology. The proposed models fit better than the conventional APC model in both examples. Conclusions: 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. ? 1995 International Epidemiological Association. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-0029093499&doi=10.1093%2fije%2f24.4.671&partnerID=40&md5=dc701993c581e637762556e6228a86da https://scholars.lib.ntu.edu.tw/handle/123456789/521749 |
ISSN: | 0300-5771 | DOI: | 10.1093/ije/24.4.671 | SDG/關鍵字: | adult; aged; article; cancer; carcinogenesis; cohort analysis; computer program; data analysis; human; methodology; model; mortality; priority journal; Adult; Age Factors; Aged; Aged, 80 and over; China; Cohort Effect; Human; Lung Neoplasms; Male; Middle Age; Models, Biological; Models, Statistical; Poisson Distribution; Prostatic Neoplasms; Software; Time Factors |
顯示於: | 流行病學與預防醫學研究所 |
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