Estimation of life expectancies using continuous-time multi-state models. (September 2019)

• Record Type: Journal Article
• Authors: van den Hout, Ardo; Sum Chan, Mei; Matthews, Fiona
• Is Part Of: Computer methods and programs in biomedicine. Volume 178(2019)
• Page Start: 11
• Page End: 18
• Publication Date: 2019-09
• Subjects: Gompertz distribution -- Interval censoring -- Markov model -- Panel data -- Sojourn time -- Stochastic process; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2019.06.004
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Clear presentation on the estimation of life expectancies based on a multi-state survival model for longitudinal data. Introduction and illustration of a new R package: 'elect'. Extended functionality in 'elect' for more complex models, such as hidden-Markov models. Wide scope of applications: multi-state survival models can applied in medical statistics, epidemiology, biology, actuarial science, demography, and social sciences; see also the references. Contributes to the growing field of ageing research: healthy life expectancy is increasingly used to quantify population health. 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 newR packageelect provides user-friendly functions to do the computation in theR software. Results: The estimation of life expectancies is explained and illustrated using theelect 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 theelect package will help researchers to compute life expectancies and to present their findings in an assessable way.