IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0226678.html
   My bibliography  Save this article

Age-period-cohort analysis with a constant-relative-variation constraint for an apportionment of period and cohort slopes

Author

Listed:
  • Shih-Yung Su
  • Wen-Chung Lee

Abstract

Age-period-cohort analysis of incidence and/or mortality data has received much attention in the literature. To circumvent the non-identifiability problem inherent in the age-period-cohort model, additional constraints are necessary on the parameters estimates. We propose setting the constraint to reflect the different nature of the three temporal variables: age, period, and birth cohort. There are two assumptions in our method. Recognizing age effects to be deterministic (first assumption), we do not explicitly incorporate the age parameters into constraint. For the stochastic period and cohort effects, we set a constant-relative-variation constraint on their trends (second assumption). The constant-relative-variation constraint dictates that between two stochastic effects, one with a larger curvature gets a larger (absolute) slope, and one with zero curvature gets no slope. We conducted Monte-Carlo simulations to examine the statistical properties of the proposed method and analyzed the data of prostate cancer incidence for whites from 1973–2012 to illustrate the methodology. A driver for the period and/or cohort effect may be lacking in some populations. In that case, the CRV method automatically produces an unbiased age effect and no period and/or cohort effect, thereby addressing the situation properly. However, the method proposed in this paper is not a general purpose model and will produce biased results in many other real-life data scenarios. It is only useful in situations when the age effects are deterministic and dominant, and the period and cohort effects are stochastic and minor.

Suggested Citation

  • Shih-Yung Su & Wen-Chung Lee, 2019. "Age-period-cohort analysis with a constant-relative-variation constraint for an apportionment of period and cohort slopes," PLOS ONE, Public Library of Science, vol. 14(12), pages 1-21, December.
    • Handle: RePEc:plo:pone00:0226678
    DOI: 10.1371/journal.pone.0226678
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0226678
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0226678&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0226678?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Wenjiang Fu, 2016. "Constrained Estimators and Consistency of a Regression Model on a Lexis Diagram," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(513), pages 180-199, March.
    2. Liying Luo, 2013. "Assessing Validity and Application Scope of the Intrinsic Estimator Approach to the Age-Period-Cohort Problem," Demography, Springer;Population Association of America (PAA), vol. 50(6), pages 1945-1967, December.
    3. Manfred Grotenhuis & Ben Pelzer & Liying Luo & Alexander W. Schmidt-Catran, 2016. "The Intrinsic Estimator, Alternative Estimates, and Predictions of Mortality Trends: A Comment on Masters, Hummer, Powers, Beck, Lin, and Finch," Demography, Springer;Population Association of America (PAA), vol. 53(4), pages 1245-1252, August.
    4. Liying Luo, 2013. "Paradigm Shift in Age-Period-Cohort Analysis: A Response to Yang and Land, O’Brien, Held and Riebler, and Fienberg," Demography, Springer;Population Association of America (PAA), vol. 50(6), pages 1985-1988, December.
    5. Stephen Fienberg, 2013. "Cohort Analysis’ Unholy Quest: A Discussion," Demography, Springer;Population Association of America (PAA), vol. 50(6), pages 1981-1984, December.
    6. Yu-Kang Tu & George Davey Smith & Mark S Gilthorpe, 2011. "A New Approach to Age-Period-Cohort Analysis Using Partial Least Squares Regression: The Trend in Blood Pressure in the Glasgow Alumni Cohort," PLOS ONE, Public Library of Science, vol. 6(4), pages 1-9, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ethan Fosse & Christopher Winship, 2019. "Bounding Analyses of Age-Period-Cohort Effects," Demography, Springer;Population Association of America (PAA), vol. 56(5), pages 1975-2004, October.
    2. Andrew Bell & Kelvyn Jones, 2014. "Another 'futile quest'? A simulation study of Yang and Land's Hierarchical Age-Period-Cohort model," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 30(11), pages 333-360.
    3. Liying Luo & John Robert Warren, 2023. "Describing and explaining age, period, and cohort trends in Americans’ vocabulary knowledge," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 42(3), pages 1-34, June.
    4. Liying Luo & James Hodges, 2019. "The Age-Period-Cohort-Interaction Model for Describing and Investigating Inter-Cohort Deviations and Intra-Cohort Life-Course Dynamics," Papers 1906.08357, arXiv.org.
    5. Ryan Masters & Robert Hummer & Daniel Powers & Audrey Beck & Shih-Fan Lin & Brian Finch, 2014. "Long-Term Trends in Adult Mortality for U.S. Blacks and Whites: An Examination of Period- and Cohort-Based Changes," Demography, Springer;Population Association of America (PAA), vol. 51(6), pages 2047-2073, December.
    6. Maarten J. Bijlsma & Rhian M. Daniel & Fanny Janssen & Bianca L. De Stavola, 2017. "An Assessment and Extension of the Mechanism-Based Approach to the Identification of Age-Period-Cohort Models," Demography, Springer;Population Association of America (PAA), vol. 54(2), pages 721-743, April.
    7. Andrew Bell & Kelvyn Jones, 2015. "Bayesian informative priors with Yang and Land’s hierarchical age–period–cohort model," Quality & Quantity: International Journal of Methodology, Springer, vol. 49(1), pages 255-266, January.
    8. Manfred Grotenhuis & Ben Pelzer & Liying Luo & Alexander W. Schmidt-Catran, 2016. "The Intrinsic Estimator, Alternative Estimates, and Predictions of Mortality Trends: A Comment on Masters, Hummer, Powers, Beck, Lin, and Finch," Demography, Springer;Population Association of America (PAA), vol. 53(4), pages 1245-1252, August.
    9. Arkadiusz Wiśniowski & Peter Smith & Jakub Bijak & James Raymer & Jonathan Forster, 2015. "Bayesian Population Forecasting: Extending the Lee-Carter Method," Demography, Springer;Population Association of America (PAA), vol. 52(3), pages 1035-1059, June.
    10. Douglas D Gunzler & Adam T Perzynski & Neal V Dawson & Kelley Kauffman & Jintao Liu & Jarrod E Dalton, 2019. "Risk-period-cohort approach for averting identification problems in longitudinal models," PLOS ONE, Public Library of Science, vol. 14(7), pages 1-17, July.
    11. Andrew Bell & Kelvyn Jones, 2015. "Bayesian informative priors with Yang and Land’s hierarchical age–period–cohort model," Quality & Quantity: International Journal of Methodology, Springer, vol. 49(1), pages 255-266, January.
    12. Zoë Fannon & B. Nielsen, 2018. "Age-period cohort models," Economics Papers 2018-W04, Economics Group, Nuffield College, University of Oxford.
    13. Maarten J. Bijlsma & Rhian Daniel & Fanny Janssen & Bianca De Stavola, 2016. "An assessment and extension of the mechanism-based approach to the identification of age-period-cohort models," MPIDR Working Papers WP-2016-005, Max Planck Institute for Demographic Research, Rostock, Germany.
    14. Enrique Acosta & Stacey A. Hallman & Lisa Y. Dillon & Nadine Ouellette & Robert Bourbeau & D. Ann Herring & Kris Inwood & David J. D. Earn & Joaquin Madrenas & Matthew S. Miller & Alain Gagnon, 2019. "Determinants of Influenza Mortality Trends: Age-Period-Cohort Analysis of Influenza Mortality in the United States, 1959–2016," Demography, Springer;Population Association of America (PAA), vol. 56(5), pages 1723-1746, October.
    15. Patrick Denice, 2017. "Back to School: Racial and Gender Differences in Adults’ Participation in Formal Schooling, 1978–2013," Demography, Springer;Population Association of America (PAA), vol. 54(3), pages 1147-1173, June.
    16. Page, Andrew & Milner, Allison & Morrell, Stephen & Taylor, Richard, 2013. "The role of under-employment and unemployment in recent birth cohort effects in Australian suicide," Social Science & Medicine, Elsevier, vol. 93(C), pages 155-162.
    17. Okoampah, Sarah, 2016. "Cohort size effects on wages, working status, and work time," Ruhr Economic Papers 629, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    18. Ryan K. Masters & Daniel A. Powers & Robert A. Hummer & Audrey Beck & Shih-Fan Lin & Brian Karl Finch, 2016. "Fitting Age-Period-Cohort Models Using the Intrinsic Estimator: Assumptions and Misapplications," Demography, Springer;Population Association of America (PAA), vol. 53(4), pages 1253-1259, August.
    19. Haoyu Wen & Cong Xie & Lu Wang & Fang Wang & Yafeng Wang & Xiaoxue Liu & Chuanhua Yu, 2019. "Difference in Long-Term Trends in COPD Mortality between China and the U.S., 1992–2017: An Age–Period–Cohort Analysis," IJERPH, MDPI, vol. 16(9), pages 1-15, April.
    20. Enrique Acosta & Alain Gagnon & Nadine Ouellette & Robert R. Bourbeau & Marilia R. Nepomuceno & Alyson A. van Raalte, 2020. "The boomer penalty: excess mortality among baby boomers in Canada and the United States," MPIDR Working Papers WP-2020-003, Max Planck Institute for Demographic Research, Rostock, Germany.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0226678. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.