Forecasting Causes of Death using Compositional Data Analysis: the Case of Cancer Deaths

Publikation: Working paperForskning

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  • rp19_07

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  • Søren Kjærgaard, Interdisciplinary Centre on Population Dynamics, University of Southern Denmark, Danmark
  • Yunus Emre Ergemen
  • Malene Kallestrup-Lamb
  • Jim Oeppen, Interdisciplinary Centre on Population Dynamics, University of Southern Denmark, Danmark
  • Rune Lindahl-Jacobsen, Interdisciplinary Centre on Population Dynamics, University of Southern Denmark, Danmark
Cause-specific mortality forecasting is often based on predicting cause-specific death rates independently. Only a few methods have been suggested that incorporate dependence among causes. An attractive alternative is to model and forecast cause-specific death distributions, rather than mortality rates, as dependence among 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 among different causes of death increased the forecast accuracy of cancer deaths using data for French and Dutch populations
OriginalsprogEngelsk
UdgivelsesstedAarhus
UdgiverInstitut for Økonomi, Aarhus Universitet
Antal sider31
StatusUdgivet - 9 maj 2019
SerietitelCREATES Research Papers
Nummer2019-07

    Forskningsområder

  • Cause-specific mortality, Cancer forecast, Forecasting methods, Compositional Data Analysis, Population health

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