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

被引：36

作者：

van den Hout, Ardo ^{[1
]}

Chan, Mei Sum ^{[2
,3
]}

Matthews, Fiona ^{[4
]}

机构：

[1] UCL, Dept Stat Sci, Gower St, London WC1E 6BT, England

[2] Univ Coll London, Oxford, England

[3] Univ Oxford, Oxford, England

[4] Newcastle Univ, Newcastle Upon Tyne, Tyne & Wear, England

来源：

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE | 2019年 / 178卷

基金：

英国医学研究理事会;

关键词：

Gompertz distribution; Interval censoring; Markov model; Panel data; Sojourn time; Stochastic process; PANEL-DATA;

D O I：

10.1016/j.cmpb.2019.06.004

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

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 multistate 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 Copyright (C) 2019 Published by Elsevier B.V. All rights reserved.

引用

页码：11 / 18

页数：8

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