Models for temporal variation in cancer rates. I: Age–period and age–cohort models

Clayton, David; Schifflers, E

doi:10.1002/sim.4780060405

Cited by 602 publications

(450 citation statements)

References 16 publications

Supporting

Mentioning

433

Contrasting

Unclassified

Order By: Relevance

“…Application of the full model for the presentation of age, period and cohort effects requires a further assumption in order to obtain unique parameter estimates (Clayton & Schifflers, 1987). As cohort effects were more important, i.e.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Trends in breast cancer incidence in Sweden 1958-1988 by time period and birth cohort

Persson

Bergström²,

Sparén³

et al. 1993

Br J Cancer

View full text Add to dashboard Cite

Summary Statistics from the Swedish National Cancer Registry based on all 110,658 cases of invasive breast cancer during the 31-year period were analysed. Age-specific incidence rates increased over successive calendar periods. The average annual increase in the age-standardised incidence rate was 1.3%, with the greatest percentage changes among the youngest age groups. During the latter half of the study period, the rates of increase tended to diminish in the youngest age groups and even reversed significantly among women from 75 years of age. In analyses using age-period-cohort models, the best fit of the cancer incidence data was found for the full model which simultaneously considered the effects of age, period and cohort. Cohort effects were found to be more important than period effects, in terms of model fit. These effects emerged as a seemingly consistent, and in a logarithmic scale, fairly linear increase in the relative risk of breast cancer incidence with a 3-fold elevation in women born in the 1950's relative to those born in the 1880's It is concluded that the rising breast cancer incidence in Sweden is explained chiefly by birth cohort effects, which indicate persistent secular changes in largely unknown risk factors associated with life style. We could not in the present data see any clear evidence for an adverse effect of contraceptive or replacement sex steroids on breast cancer incidence.

show abstract

Section: Resultsmentioning

confidence: 99%

“…As cohort effects were more important, i.e. the age plus cohort model produced a better fit than the age plus period model (Table IV), we imposed the restriction that the linear period effect was assumed to be zero (Clayton & Schifflers, 1987). Cohort effects were computed according to this approach.…”

Section: Resultsmentioning

confidence: 99%

Trends in breast cancer incidence in Sweden 1958-1988 by time period and birth cohort

Persson

Bergström²,

Sparén³

et al. 1993

Br J Cancer

View full text Add to dashboard Cite

show abstract

“…The deviance statistics and Z-test were used to determine the goodness of fit of the models and significance of the effects, respectively. 11 The deviance statistic measures the closeness of the model predictions to the observed rates; hence, a nonsignificant value indicates a good fit. The Akaike information criterion (AIC) was also calculated as it enables the comparison of models with different complexity, with smaller values indicating better fit.…”

Section: Discussionmentioning

confidence: 99%

“…In APC modeling, there is also a criticism when comparing agecohort and age-period models that the age-cohort model will provide a better fit simply because it offers additional complexity due to the larger number of terms. 11 The AIC provides a method for comparing models that adjusts for this unequal complexity. In 3 of our 4 analyses presented here, the AIC was smaller for the age-cohort model, indicating a better fit, while in the fourth (Sweden, age 45 and older), the AIC for the age-cohort model was only marginally higher than for the age-period model, with the p-values for the fits being similar.…”

Section: Discussionmentioning

confidence: 99%

Profound changes in breast cancer incidence may reflect changes into a Westernized lifestyle: A comparative population‐based study in Singapore and Sweden

Chia

Reilly

Tan

et al. 2004

Intl Journal of Cancer

110

View full text Add to dashboard Cite

Breast cancer incidence in Sweden has always been approximately twice as high as in Singapore. In recent years, this difference is limited to postmenopausal women. The aim of this study was to explore the reasons behind these differences through the use of age-period-cohort modeling. This population-based study included all breast cancer cases reported to the Swedish and the Singapore cancer registries from 1968 to 1997, with a total of 135,581 Swedish and 10,716 Singaporean women. Poisson regression using ageperiod and age-cohort models was used to determine the effects of age at diagnosis, calendar period and birth cohort. Incidence rate ratios were used to summarize these effects. An age-cohort model provided the best fit to the data in both countries, indicating that changes over lifetime, rather than recent differences in medical surveillance, might account for the observed differences in these 2 populations. The changes over birth cohort were much greater among Singaporean women. The relative effect of age was very similar in the 2 countries. Analyses show that age and cohort effects may explain the differences in trends of female breast cancer incidence between Sweden and Singapore. The larger cohort effect seen in Singaporean women may be attributed to more rapid changes in reproduction and lifestyle patterns than that of Swedish women during the period studied. The incidence of breast cancer in postmenopausal women in Singapore will probably continue to rise in the coming decades to match the current Swedish rates.

show abstract

“…To determine whether a cohort or period effect (or both) is predominant in the evolution of incidence or mortality, we used the method described by Clayton and Schifflers. 19,20 Briefly, the principle of this method is to perform successively age, age-period, age-cohort and age-period-cohort models using Poisson regression (maximum likelihood method), and to choose the model with regard to its deviance and to the contribution of the different variables (log likelihood ratio test). If R ijk is the rate (incidence or mortality) for the ith of age interval in the jth of calendar year periods, where k indexes the corresponding birth-cohort, the usual age-period-cohort model is…”

Section: Methodsmentioning

confidence: 99%

Incidence and mortality trends for prostate cancer in 5 French areas from 1982 to 1996

Chirpaz

Colonna²,

Ménegoz³

et al. 2001

Intl Journal of Cancer

View full text Add to dashboard Cite

After an increase in the 1980s, incidence and mortality for prostate cancer in North America or England and Wales started to decrease in the early 1990s. The reasons for this evolution are widely debated, notably the importance of early detection. This study describes trends of prostate cancer incidence and mortality in 5 areas in France, where practices of early detection for this cancer are widely used. The 5 French administrative areas, covered by a population-based registry, have a total population of approximately 1,700,000 men. Incidence data from these registries were studied for the period 1982-1995, and mortality data were provided by the Institut National de la Santé et de la Recherche Médicale (INSERM) for the period 1982-1996. Age-Period-Cohort models by Poisson regression were created to characterize these trends. Between 1982 and 1995, 14,699 cases of prostate cancer were registered by the 5 registries under consideration. After a little intensification of the increase in 1987, undoubtedly due to early detection (notably using ProstateSpecific Antigen), the trend of the incidence seems to reverse from 1993. Mortality increased monotonically from 1982-1990 by an average of 1.8% per year, before decreasing annually by an average of 3.3% until 1996. Poisson regressions indicated a period effect on both incidence and mortality data; a small, but significant, cohort effect exists for incidence evolution, showing that elements such as etiologic factors may have an influence. Until results of randomized studies on mass screening are available, the question of individual screening remains; improved knowledge of risk factors could be interesting.

show abstract

Models for temporal variation in cancer rates. I: Age–period and age–cohort models

Cited by 602 publications

References 16 publications

Trends in breast cancer incidence in Sweden 1958-1988 by time period and birth cohort

Trends in breast cancer incidence in Sweden 1958-1988 by time period and birth cohort

Profound changes in breast cancer incidence may reflect changes into a Westernized lifestyle: A comparative population‐based study in Singapore and Sweden

Incidence and mortality trends for prostate cancer in 5 French areas from 1982 to 1996

Contact Info

Product

Resources

About