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

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.

Sex differences in healthy life expectancy among nonagenarians: A multistate survival model using data from the Vitality 90+ study

“…Strengths of the study include the large sample of nonagenarians, the use of an entire age cohort from one area, the long follow-up period, and the novel analytical approach. The multistate survival model approach is among the few statistical methods that does not require national life table data to calculate LEs [12,19], as opposed to the commonly used Sullivan method [20]. Using the ELECT package in R, LEs may be calculated in any longitudinal dataset with information on health states and mortality.…”

“…The multistate survival models were estimated using the MSM package for R [16]. Based on the parameters of the multistate models, LEs were calculated using the ELECT (Estimating Life Expectancies in Continuous Time) package for R [12]. ELECT estimates total and marginal LEs based on multinomial regression models for state prevalence.…”

“…Recent developments in multistate modeling make it possible to study the role of risk factors, such as sex, in transitions between these states [11]. Moreover, based on multistate models, overall, healthy, and unhealthy life expectancies (LEs) may be estimated for specific groups [12]. The objective of this study was to examine sex differences in total, healthy and unhealthy life expectancy in a population of nonagenarians (i.e., people aged 90 years and over).…”

Working life expectancy in good and poor self-perceived health among Dutch workers aged 55–65 years with a chronic disease over the period 1992–2016

“…Differences between WLEs were considered statistically significant when the point estimate of one WLE was not within the 95% CI of the other WLE, and the other way around. WLEs are also graphically presented 17. The graphs show WLEs in good and poor self-perceived health on the y-axis for age 55–68 years presented on the x-axis, for workers with a chronic disease in general and for workers who initially have poor self-perceived health, respectively.…”

Occupational Exposures Associated with Life Expectancy without and with Disability

“…Hazard ratios (HR) and transition probabilities were derived from these hazards. In the second step, transition probabilities were used to estimate total life expectancies (LE) at the age of 55 years as well as LE without and with disability using the R-package Estimating Life Expectancies using Continuous Time (ELECT) [21]. LEs are reported separately for low and high exposure to any of the physical and psychosocial work demands and resources and separately for men and women.…”