1987
DOI: 10.1002/sim.4780060406
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Models for temporal variation in cancer rates. II: Age–period–cohort models

Abstract: Our first paper reviewed methods for modelling variation in cancer incidence and mortality rates in terms of either period effects or cohort effects in the general multiplicative risk model. There we drew attention to the difficulty of attributing regular trends to either period or cohort influences. In this paper we turn to the more realistic problem in which neither period nor cohort effects alone lead to an adequate description of the data. We describe the age-period+ohort model and show how its ambiguities… Show more

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Cited by 773 publications
(634 citation statements)
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“…The good fit of a 2-term age-cohort model to our data enables us to interpret and compare the effects, but it is important that one first checks for linear drift, which can manifest as any age, period, or cohort effect. 25 In 3 of our 4 analyses, an age-drift model did not fit the data, and in the fourth (Sweden, age Ͻ 45) there was only marginal evidence (p ϭ 0.08) of fit. Furthermore, in all analyses, age-cohort models provided adequate fit and were significantly better than simple linear (drift) models.…”
Section: Discussionmentioning
confidence: 70%
“…The good fit of a 2-term age-cohort model to our data enables us to interpret and compare the effects, but it is important that one first checks for linear drift, which can manifest as any age, period, or cohort effect. 25 In 3 of our 4 analyses, an age-drift model did not fit the data, and in the fourth (Sweden, age Ͻ 45) there was only marginal evidence (p ϭ 0.08) of fit. Furthermore, in all analyses, age-cohort models provided adequate fit and were significantly better than simple linear (drift) models.…”
Section: Discussionmentioning
confidence: 70%
“…Predictions of future cancer patterns use a variety of modelling strategies (Teppo et al, 1974;Osmond, 1985;Clayton and Schifflers, 1987;Hakulinen and Dyba, 1994;Hakulinen, 1996;Dyba and Hakulinen, 2000), their reliability depending directly on the choice of model and the explanatory variables fitted. In the absence of information on the trends in exposure to relevant risk factors, the most practical and plausible method is to use simple time-linear models on the arithmetic or logarithmic scale (Dyba and Hakulinen, 2000).…”
Section: Discussionmentioning
confidence: 99%
“…A formal statistical examination of whether trends were better described by secular changes in risk (period effects) or changes in risk from generation to generation (birth cohort effects) was then made by fitting the standard age period-cohort model. 23 The models fitted included terms for age, drift (the rate of change of regular trend, which cannot be attributed exclusively to either period and cohort) and the nonlinear effects of period and of cohort. Hierarchic fitting strategies were employed to determine the best model.…”
Section: Time Trends In Incidence and Mortalitymentioning
confidence: 99%
“…Hierarchic fitting strategies were employed to determine the best model. 23 To examine the effects of period and cohort as measures of relative risk, the groups with midpoints 1882 and 1914 were used as baselines, respectively.…”
Section: Time Trends In Incidence and Mortalitymentioning
confidence: 99%