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

Søren Kjærgaard, Yunus Emre Ergemen, Malene Kallestrup-Lamb, James Oeppen, Rune Lindahl-Jacobsen

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

Resumé

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.
OriginalsprogEngelsk
TidsskriftJournal of the Royal Statistical Society, Series C (Applied Statistics)
Vol/bind68
Udgave nummer5
Sider (fra-til)1351-1370
ISSN0035-9254
DOI
StatusUdgivet - 13. jun. 2019

Fingeraftryk

Compositional Data
Mortality
Forecast
Forecasting
Data analysis
Cancer
Mortality Rate
Alternatives
Model
Cause of death
Mortality forecasting

Citer dette

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Forecasting causes of death by using compositional data analysis: the case of cancer deaths. / Kjærgaard, Søren; Ergemen, Yunus Emre; Kallestrup-Lamb, Malene; Oeppen, James; Lindahl-Jacobsen, Rune.

I: Journal of the Royal Statistical Society, Series C (Applied Statistics), Bind 68, Nr. 5, 13.06.2019, s. 1351-1370.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

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AU - Kjærgaard, Søren

AU - Ergemen, Yunus Emre

AU - Kallestrup-Lamb, Malene

AU - Oeppen, James

AU - Lindahl-Jacobsen, Rune

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AB - 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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