Ardo van den Hout1, Mei Sum Chan2, Fiona Matthews3. 1. Department of Statistical Science, University College London Gower Street, London WC1E 6BT, UK. Electronic address: ardo.vandenhout@ucl.ac.uk. 2. University College London and University of Oxford, UK. 3. Newcastle University, UK.
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. Crown
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. Crown
Authors: Tomiko Yoneda; Eileen Graham; Tristen Lozinski; David A Bennett; Daniel Mroczek; Andrea M Piccinin; Scott M Hofer; Graciela Muniz-Terrera Journal: J Pers Soc Psychol Date: 2022-04-11
Authors: Andreas Höhn; Stuart J McGurnaghan; Thomas M Caparrotta; Anita Jeyam; Joseph E O'Reilly; Luke A K Blackbourn; Sara Hatam; Christian Dudel; Rosie J Seaman; Joseph Mellor; Naveed Sattar; Rory J McCrimmon; Brian Kennon; John R Petrie; Sarah Wild; Paul M McKeigue; Helen M Colhoun Journal: PLoS One Date: 2022-08-11 Impact factor: 3.752