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Forecasting causes of death by using compositional data analysis: the case of cancer deaths

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  • Søren Kjærgaard
  • Yunus Emre Ergemen
  • Malene Kallestrup‐Lamb
  • Jim Oeppen
  • Rune Lindahl‐Jacobsen

Abstract

Cause‐specific mortality forecasting is often based on predicting cause‐specific death rates independently. Only a few methods have been suggested that incorporate dependence between causes. An attractive alternative is to model and forecast cause‐specific death distributions, rather than mortality rates, as dependence between the causes can be incorporated directly. We follow this idea and propose two new models which extend the current research on mortality forecasting using death distributions. We find that adding age, time and cause‐specific weights and decomposing both joint and individual variation between different causes of death increased the forecast accuracy of cancer deaths by using data for French and Dutch populations.

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  • Søren Kjærgaard & Yunus Emre Ergemen & Malene Kallestrup‐Lamb & Jim Oeppen & Rune Lindahl‐Jacobsen, 2019. "Forecasting causes of death by using compositional data analysis: the case of cancer deaths," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 68(5), pages 1351-1370, November.
    • Handle: RePEc:bla:jorssc:v:68:y:2019:i:5:p:1351-1370
    DOI: 10.1111/rssc.12357
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    Cited by:

    1. Marie-Pier Bergeron-Boucher & Søren Kjærgaard & James E. Oeppen & James W. Vaupel, 2019. "The impact of the choice of life table statistics when forecasting mortality," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 41(43), pages 1235-1268.
    2. Søren Kjærgaard & Yunus Emre Ergemen & Marie-Pier Bergeron Boucher & Jim Oeppen & Malene Kallestrup-Lamb, 2019. "Longevity forecasting by socio-economic groups using compositional data analysis," CREATES Research Papers 2019-08, Department of Economics and Business Economics, Aarhus University.
    3. Basellini, Ugofilippo & Camarda, Carlo Giovanni & Booth, Heather, 2022. "Thirty years on: A review of the Lee-Carter method for forecasting mortality," SocArXiv 8u34d, Center for Open Science.
    4. Rizzi, Silvia & Kjærgaard, Søren & Bergeron Boucher, Marie-Pier & Camarda, Carlo Giovanni & Lindahl-Jacobsen, Rune & Vaupel, James W., 2021. "Killing off cohorts: Forecasting mortality of non-extinct cohorts with the penalized composite link model," International Journal of Forecasting, Elsevier, vol. 37(1), pages 95-104.
    5. S⊘ren Kjærgaard & Yunus Emre Ergemen & Marie‐Pier Bergeron‐Boucher & Jim Oeppen & Malene Kallestrup‐Lamb, 2020. "Longevity forecasting by socio‐economic groups using compositional data analysis," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(3), pages 1167-1187, June.
    6. Graziani, Rebecca & NIGRI, ANDREA, 2023. "An Age–Period–Cohort Model in a Dirichlet Framework: A Coherent Causes of Death Estimation," SocArXiv 856yw, Center for Open Science.
    7. Bergeron-Boucher, Marie-Pier & Kjærgaard, Søren, 2022. "Mortality forecasts by age and cause of death: How to forecast both dimensions?," SocArXiv d7hbp, Center for Open Science.
    8. Basellini, Ugofilippo & Camarda, Carlo Giovanni & Booth, Heather, 2023. "Thirty years on: A review of the Lee–Carter method for forecasting mortality," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1033-1049.
    9. Marco Stefanucci & Stefano Mazzuco, 2022. "Analysing cause‐specific mortality trends using compositional functional data analysis," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(1), pages 61-83, January.

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