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A note on computing average state occupation times

Author

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  • Jan Beyersmann

    (Universität Ulm)

  • Hein Putter

    (Universiteit Leiden)

Abstract

Objective: This review discusses how biometricians would probably compute or estimate expected waiting times, if they had the data. Methods: Our framework is a time-inhomogeneous Markov multistate model, where all transition hazards are allowed to be time-varying. We assume that the cumulative transition hazards are given. That is, they are either known, as in a simulation, determined by expert guesses, or obtained via some method of statistical estimation. Our basic tool is product integration, which transforms the transition hazards into the matrix of transition probabilities. Product integration enjoys a rich mathematical theory, which has successfully been used to study probabilistic and statistical aspects of multistate models. Our emphasis will be on practical implementation of product integration, which allows us to numerically approximate the transition probabilities. Average state occupation times and other quantities of interest may then be derived from the transition probabilities.

Suggested Citation

  • Jan Beyersmann & Hein Putter, 2014. "A note on computing average state occupation times," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 30(62), pages 1681-1696.
    • Handle: RePEc:dem:demres:v:30:y:2014:i:62
    DOI: 10.4054/DemRes.2014.30.62
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    2. Jacob Pedersen & Svetlana Solovieva & Sannie Vester Thorsen & Malene Friis Andersen & Ute Bültmann, 2021. "Expected Labor Market Affiliation: A New Method Illustrated by Estimating the Impact of Perceived Stress on Time in Work, Sickness Absence and Unemployment of 37,605 Danish Employees," IJERPH, MDPI, vol. 18(9), pages 1-13, May.

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    More about this item

    Keywords

    event history analysis; multistate models;

    JEL classification:

    • J1 - Labor and Demographic Economics - - Demographic Economics
    • Z0 - Other Special Topics - - General

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