Literature DB >> 6626659

The estimation of age, period and cohort effects for vital rates.

T R Holford.   

Abstract

In models for vital rates which include effects due to age, period and cohort, there is aliasing due to a linear dependence among these three factors. This dependence arises both when age and period intervals are equal and when they are not. One solution to the dependence is to set an arbitrary constraint on the parameters. Estimable functions of the parameters are invariant to the particular constraint applied. For evenly spaced intervals, deviations from linearity are estimable but only a linear function of the three slopes is estimable. When age and period intervals have different widths, further aliasing occurs. It is assumed that the number of deaths in the numerator of the rate equation has a Poisson distribution. The calculations are illustrated with data on mortality from prostate cancer among nonwhites in the U.S.

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Year:  1983        PMID: 6626659

Source DB:  PubMed          Journal:  Biometrics        ISSN: 0006-341X            Impact factor:   2.571


  129 in total

1.  Secular trends in adolescent never smoking from 1990 to 1999 in California: an age-period-cohort analysis.

Authors:  Xinguang Chen; Guohua Li; Jennifer B Unger; Xiaowei Liu; C Anderson Johnson
Journal:  Am J Public Health       Date:  2003-12       Impact factor: 9.308

2.  Age-period-cohort analysis of cancers not related to tobacco, screening, or HIV: sex and race differences.

Authors:  Yueh-Ying Han; Gregg E Dinse; David M Umbach; Devra L Davis; Joel L Weissfeld
Journal:  Cancer Causes Control       Date:  2010-04-07       Impact factor: 2.506

3.  Common Model Inputs Used in CISNET Collaborative Breast Cancer Modeling.

Authors:  Jeanne S Mandelblatt; Aimee M Near; Diana L Miglioretti; Diego Munoz; Brian L Sprague; Amy Trentham-Dietz; Ronald Gangnon; Allison W Kurian; Harald Weedon-Fekjaer; Kathleen A Cronin; Sylvia K Plevritis
Journal:  Med Decis Making       Date:  2018-04       Impact factor: 2.583

4.  Patterns of birth cohort-specific smoking histories, 1965-2009.

Authors:  Theodore R Holford; David T Levy; Lisa A McKay; Lauren Clarke; Ben Racine; Rafael Meza; Stephanie Land; Jihyoun Jeon; Eric J Feuer
Journal:  Am J Prev Med       Date:  2014-02       Impact factor: 5.043

5.  Breast cancer in Portugal: Temporal trends and age-specific incidence by geographic regions.

Authors:  Gonçalo Forjaz de Lacerda; Scott P Kelly; Joana Bastos; Clara Castro; Alexandra Mayer; Angela B Mariotto; William F Anderson
Journal:  Cancer Epidemiol       Date:  2018-03-13       Impact factor: 2.984

6.  Gender is an age-specific effect modifier for papillary cancers of the thyroid gland.

Authors:  Briseis A Kilfoy; Susan S Devesa; Mary H Ward; Yawei Zhang; Philip S Rosenberg; Theodore R Holford; William F Anderson
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2009-03-17       Impact factor: 4.254

7.  Continuing female predominance in depressive illness.

Authors:  A C Leon; G L Klerman; P Wickramaratne
Journal:  Am J Public Health       Date:  1993-05       Impact factor: 9.308

8.  Calibration of disease simulation model using an engineering approach.

Authors:  Chung Yin Kong; Pamela M McMahon; G Scott Gazelle
Journal:  Value Health       Date:  2009-01-12       Impact factor: 5.725

9.  Case Studies of Gastric, Lung, and Oral Cancer Connect Etiologic Agent Prevalence to Cancer Incidence.

Authors:  Andrew F Brouwer; Marisa C Eisenberg; Rafael Meza
Journal:  Cancer Res       Date:  2018-06-15       Impact factor: 12.701

10.  Generational risks for cancers not related to tobacco, screening, or treatment in the United States.

Authors:  Yueh-Ying Han; Devra L Davis; Joel L Weissfeld; Gregg E Dinse
Journal:  Cancer       Date:  2010-02-15       Impact factor: 6.860

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