Literature DB >> 25715831

Age-Period-Cohort approaches to back-calculation of cancer incidence rate.

Cheongeun Oh1, Theodore R Holford.   

Abstract

A compartment model for cancer incidence and mortality is developed in which healthy subjects may develop cancer and subsequently die of cancer or another cause. In order to adequately represent the experience of a defined population, it is also necessary to allow for subjects who are diagnosed at death, as well as subjects who migrate and are subsequently lost to follow-up. Expressions are derived for the number of cancer deaths as a function of the number of incidence cases and vice versa, which allows for the use of mortality statistics to obtain estimates of incidence using survival information. In addition, the model can be used to obtain estimates of cancer prevalence, which is useful for health care planning. The method is illustrated using data on lung cancer among males in Connecticut.
Copyright © 2015 John Wiley & Sons, Ltd.

Entities:  

Keywords:  age-period-cohort model; back-calculation; incidence; mortality; survival

Mesh:

Year:  2015        PMID: 25715831      PMCID: PMC4980760          DOI: 10.1002/sim.6464

Source DB:  PubMed          Journal:  Stat Med        ISSN: 0277-6715            Impact factor:   2.373


  23 in total

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Authors:  R A CASE
Journal:  Br J Prev Soc Med       Date:  1956-10

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Authors:  A Verdecchia; R Capocaccia; V Egidi; A Golini
Journal:  Stat Med       Date:  1989-02       Impact factor: 2.373

Review 3.  The potential and limitations of data from population-based state cancer registries.

Authors:  J N Izquierdo; V J Schoenbach
Journal:  Am J Public Health       Date:  2000-05       Impact factor: 9.308

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Authors:  D R Brillinger
Journal:  Biometrics       Date:  1986-12       Impact factor: 2.571

5.  Age, period and cohort models: the use of individual records.

Authors:  C Robertson; P Boyle
Journal:  Stat Med       Date:  1986 Sep-Oct       Impact factor: 2.373

6.  Relationships between incidence and mortality in non-reversible diseases.

Authors:  R Capocaccia
Journal:  Stat Med       Date:  1993-12-30       Impact factor: 2.373

7.  The use of mortality time series data to produce hypothetical morbidity distributions and projects mortality trends.

Authors:  K G Manton; E Stallard
Journal:  Demography       Date:  1982-05

8.  The analysis of rates and of survivorship using log-linear models.

Authors:  T R Holford
Journal:  Biometrics       Date:  1980-06       Impact factor: 2.571

9.  A hierarchical Bayesian approach to age-specific back-calculation of cancer incidence rates.

Authors:  M Mezzetti; C Robertson
Journal:  Stat Med       Date:  1999-04-30       Impact factor: 2.373

10.  Multistage carcinogenesis and lung cancer mortality in three cohorts.

Authors:  William D Hazelton; Mark S Clements; Suresh H Moolgavkar
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2005-05       Impact factor: 4.254

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