Literature DB >> 9126501

A comparison of three methods of analysis for age-period-cohort models with application to incidence data on non-Hodgkin's lymphoma.

R J McNally1, F E Alexander, A Staines, R A Cartwright.   

Abstract

BACKGROUND: Various methods of analysis have been used to study age-period-cohort models. The main aim of this paper is to illustrate and compare three such methods. Those of Clayton and Schifflers, Robertson and Boyle, and De Carli and La Vecchia. The main differences between these methods lie in their approach to distinguish between linear-period and linear-cohort effects. Clayton and Schifflers do not attempt to solve this identification problem, whereas Robertson and Boyle, and De Carli and La Vecchia attempt to tackle this question.
METHODS: In order to study the assumptions and problems of these methods, we analysed data from 2678 subjects aged 30-84 in Yorkshire, UK, who were diagnosed with non-Hodgkin's lymphoma (NHL) during the period 1978-1991. Loglinear Poisson models were used to examine the effects of age, period and cohort.
RESULTS: All three methods of analysis agree that, after stratification for sex and county, the age-standardized rate has been increasing at about 5% per year. The Robertson-Boyle method differed from the Clayton-Schifflers method in showing a significant non-linear cohort effect, and a significant county-cohort interaction. The method of De Carli-La Vecchia agreed more closely with Clayton-Schifflers than with Robertson-Boyle.
CONCLUSIONS: The linear increase in incidence would lead to a doubling of the number of cases within 15 years. There is controversy over whether the identification problem can be solved and should be solved. Many authors would not rely on the results of the methods of Robertson and Boyle, or De Carli and La Vacchia.

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Year:  1997        PMID: 9126501     DOI: 10.1093/ije/26.1.32

Source DB:  PubMed          Journal:  Int J Epidemiol        ISSN: 0300-5771            Impact factor:   7.196


  15 in total

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Authors:  Nádia C P Rodrigues; Guilherme L Werneck
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5.  A multiphase method for estimating cohort effects in age-period contingency table data.

Authors:  Katherine M Keyes; Guohua Li
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8.  What is a cohort effect? Comparison of three statistical methods for modeling cohort effects in obesity prevalence in the United States, 1971-2006.

Authors:  Katherine M Keyes; Rebecca L Utz; Whitney Robinson; Guohua Li
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9.  Age-period-cohort modelling of non-Hodgkin's lymphoma incidence in a French region: a period effect compatible with an environmental exposure.

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10.  Age-period-cohort analysis of trends in amyotrophic lateral sclerosis incidence.

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