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Estimation of life expectancies using continuous-time multi-state models

Van Den Hout, A; Chan, MS; Matthews, F; (2019) Estimation of life expectancies using continuous-time multi-state models. Computer Methods and Programs in Biomedicine , 178 pp. 11-18. 10.1016/j.cmpb.2019.06.004.

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Abstract

Background and objective: There is increasing interest in multi-state modelling of health-related stochastic processes. Given a fitted multi-state model with one death state, it is possible to estimate state-specific and marginal life expectancies. This paper introduces methods and new software for computing these expectancies. // Methods: The definition of state-specific life expectancy given current age is an extension of mean survival in standard survival analysis. The computation involves the estimated parameters of a fitted multi-state model, and numerical integration. The new R package elect provides user-friendly functions to do the computation in the R software. // Results: The estimation of life expectancies is explained and illustrated using the elect package. Functions are presented to explore the data, to estimate the life expectancies, and to present results. // Conclusions: State-specific life expectancies provide a communicable representation of health-related processes. The availability and explanation of the elect package will help researchers to compute life expectancies and to present their findings in an assessable way.

Type: Article
Title: Estimation of life expectancies using continuous-time multi-state models
DOI: 10.1016/j.cmpb.2019.06.004
Publisher version: https://doi.org/10.1016/j.cmpb.2019.06.004
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Gompertz distribution, Interval censoring, Markov model, Panel data, Sojourn time, Stochastic process
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
URI: https://discovery.ucl.ac.uk/id/eprint/10075585
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