A multiple state model for the working-age disabled population using cross-sectional data

Naka, Poontavika; Boado‐Penas, María del Carmen; Lanot, Gauthier

doi:10.1080/03461238.2020.1724192

Cited by 4 publications

(1 citation statement)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ref. [20] is a simplified multiple-state model that develops a generic estimation method for calculating the transition probabilities in a one-year multiple-state model based on disability prevalence rates; multiple logistic regression models are employed to estimate disability prevalence rates and the one-year recovery rates. In doing so, the authors assume three conditions of the ratio between the mortality rate of inactive and active people-and several other conditions used in the literature-that allow the necessary computations in the case treated which concerns cross-sectional data measuring the disabled status of an individual at one point in time.…”

Section: Introduction With a Literature Reviewmentioning

confidence: 99%

Estimation–Calibration of Continuous-Time Non-Homogeneous Markov Chains with Finite State Space

Esquível,

Krasii,

Guerreiro

2024

Mathematics

View full text Add to dashboard Cite

We propose a method for fitting transition intensities to a sufficiently large set of trajectories of a continuous-time non-homogeneous Markov chain with a finite state space. Starting with simulated data computed with Gompertz–Makeham transition intensities, we apply the proposed method to fit piecewise linear intensities and then compare the transition probabilities corresponding to both the Gompertz–Makeham transition intensities and the fitted piecewise linear intensities; the main comparison result is that the order of magnitude of the average fitting error per unit time—chosen as a year—is always less than 1%, thus validating the methodology proposed.

show abstract

Section: Introduction With a Literature Reviewmentioning

confidence: 99%