IDEAS home Printed from https://ideas.repec.org/a/eee/thpobi/v73y2008i1p1-10.html
   My bibliography  Save this article

Model of hidden heterogeneity in longitudinal data

Author

Listed:
  • Yashin, Anatoli I.
  • Arbeev, Konstantin G.
  • Akushevich, Igor
  • Kulminski, Alexander
  • Akushevich, Lucy
  • Ukraintseva, Svetlana V.

Abstract

Variables measured in longitudinal studies of aging and longevity do not exhaust the list of all factors affecting health and mortality transitions. Unobserved factors generate hidden variability in susceptibility to diseases and death in populations and in age trajectories of longitudinally measured indices. Effects of such heterogeneity can be manifested not only in observed hazard rates but also in average trajectories of measured indices. Although effects of hidden heterogeneity on observed mortality rates are widely discussed, their role in forming age patterns of other aging-related characteristics (average trajectories of physiological state, stress resistance, etc.) is less clear. We propose a model of hidden heterogeneity to analyze its effects in longitudinal data. The approach takes the presence of hidden heterogeneity into account and incorporates several major concepts currently developing in aging research (allostatic load, aging-associated decline in adaptive capacity and stress-resistance, age-dependent physiological norms). Simulation experiments confirm identifiability of model's parameters.

Suggested Citation

  • Yashin, Anatoli I. & Arbeev, Konstantin G. & Akushevich, Igor & Kulminski, Alexander & Akushevich, Lucy & Ukraintseva, Svetlana V., 2008. "Model of hidden heterogeneity in longitudinal data," Theoretical Population Biology, Elsevier, vol. 73(1), pages 1-10.
    • Handle: RePEc:eee:thpobi:v:73:y:2008:i:1:p:1-10
    DOI: 10.1016/j.tpb.2007.09.001
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0040580907000949
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.tpb.2007.09.001?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. James Vaupel & Kenneth Manton & Eric Stallard, 1979. "The impact of heterogeneity in individual frailty on the dynamics of mortality," Demography, Springer;Population Association of America (PAA), vol. 16(3), pages 439-454, August.
    2. Chris Elbers & Geert Ridder, 1982. "True and Spurious Duration Dependence: The Identifiability of the Proportional Hazard Model," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 49(3), pages 403-409.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Anatoly I. Yashin & Igor Akushevich & Konstantin G. Arbeev & Alexander Kulminski & Svetlana Ukraintseva, 2011. "Joint Analysis of Health Histories, Physiological State, and Survival," Mathematical Population Studies, Taylor & Francis Journals, vol. 18(4), pages 207-233, October.
    2. Vindenes, Yngvild & Sæther, Bernt-Erik & Engen, Steinar, 2012. "Effects of demographic structure on key properties of stochastic density-independent population dynamics," Theoretical Population Biology, Elsevier, vol. 82(4), pages 253-263.

    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. Bijwaard, Govert, 2011. "Unobserved Heterogeneity in Multiple-Spell Multiple-States Duration Models," IZA Discussion Papers 5748, Institute of Labor Economics (IZA).
    2. Wienke, Andreas & Kuss, Oliver, 2009. "Random effects Weibull regression model for occupational lifetime," European Journal of Operational Research, Elsevier, vol. 196(3), pages 1249-1250, August.
    3. Govert Ewout Bijwaard, 2014. "Multistate event history analysis with frailty," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 30(58), pages 1591-1620.
    4. Jaap H. Abbring & Gerard J. Van Den Berg, 2007. "The unobserved heterogeneity distribution in duration analysis," Biometrika, Biometrika Trust, vol. 94(1), pages 87-99.
    5. Abbring, Jaap H. & Berg, Gerard J. van den, 2000. "The non-parametric identification of the mixed proportional hazards competing risks model," Serie Research Memoranda 0024, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics.
    6. Anna Damm, 2009. "Determinants of recent immigrants’ location choices: quasi-experimental evidence," Journal of Population Economics, Springer;European Society for Population Economics, vol. 22(1), pages 145-174, January.
    7. Alex Mota & Eder A. Milani & Vinicius F. Calsavara & Vera L. D. Tomazella & Jeremias Leão & Pedro L. Ramos & Paulo H. Ferreira & Francisco Louzada, 2021. "Weighted Lindley frailty model: estimation and application to lung cancer data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 27(4), pages 561-587, October.
    8. Van den Berg, Gerard J., 2001. "Duration models: specification, identification and multiple durations," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 5, chapter 55, pages 3381-3460, Elsevier.
    9. Brito, Éder S. & Tomazella, Vera L.D. & Ferreira, Paulo H., 2022. "Statistical modeling and reliability analysis of multiple repairable systems with dependent failure times under perfect repair," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    10. Mette Gerster & Niels Keiding & Lisbeth B. Knudsen & Katrine Strandberg-Larsen, 2007. "Education and second birth rates in Denmark 1981-1994," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 17(8), pages 181-210.
    11. Missov, Trifon I. & Finkelstein, Maxim, 2011. "Admissible mixing distributions for a general class of mixture survival models with known asymptotics," Theoretical Population Biology, Elsevier, vol. 80(1), pages 64-70.
    12. Su, Shu & Sherris, Michael, 2012. "Heterogeneity of Australian population mortality and implications for a viable life annuity market," Insurance: Mathematics and Economics, Elsevier, vol. 51(2), pages 322-332.
    13. Andreas Wienke & Paul Lichtenstein & Anatoli I. Yashin, 2003. "Unobserved heterogeneity in a model with cure fraction applied to breast cancer," MPIDR Working Papers WP-2003-010, Max Planck Institute for Demographic Research, Rostock, Germany.
    14. Haghani, Shermineh, 2014. "Modeling hedge fund lifetimes: A dependent competing risks framework with latent exit types," Journal of Empirical Finance, Elsevier, vol. 28(C), pages 291-320.
    15. Adriaan Kalwij, 2014. "An empirical analysis of the importance of controlling for unobserved heterogeneity when estimating the income-mortality gradient," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 31(30), pages 913-940.
    16. Steven M. Shugan, 2006. "Editorial: Errors in the Variables, Unobserved Heterogeneity, and Other Ways of Hiding Statistical Error," Marketing Science, INFORMS, vol. 25(3), pages 203-216, 05-06.
    17. Shu Su & Michael Sherris, 2011. "Heterogeneity of Australian Population Mortality and Implications for a Viable Life Annuity Market," Working Papers 201103, ARC Centre of Excellence in Population Ageing Research (CEPAR), Australian School of Business, University of New South Wales.
    18. James W. Vaupel & Trifon Missov, 2014. "Unobserved population heterogeneity," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 31(22), pages 659-686.
    19. Peng, Yingwei & Zhang, Jiajia, 2008. "Identifiability of a mixture cure frailty model," Statistics & Probability Letters, Elsevier, vol. 78(16), pages 2604-2608, November.
    20. Bagdonavicius, Vilijandas & Nikulin, Mikhail, 2000. "On goodness-of-fit for the linear transformation and frailty models," Statistics & Probability Letters, Elsevier, vol. 47(2), pages 177-188, April.

    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:eee:thpobi:v:73:y:2008:i:1:p:1-10. 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: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/intelligence .

    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.