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SemiMarkov: An R Package for Parametric Estimation in Multi-State Semi-Markov Models

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  • Król, Agnieszka
  • Saint-Pierre, Philippe

Abstract

Multi-state models provide a relevant tool for studying the observations of a continuoustime process at arbitrary times. Markov models are often considered even if semi-Markov are better adapted in various situations. Such models are still not frequently applied mainly due to lack of available software. We have developed the R package SemiMarkov to fit homogeneous semi-Markov models to longitudinal data. The package performs maximum likelihood estimation in a parametric framework where the distributions of the sojourn times can be chosen between exponential, Weibull or exponentiated Weibull. The package computes and displays the hazard rates of sojourn times and the hazard rates of the semi-Markov process. The effects of covariates can be studied with a Cox proportional hazards model for the sojourn times distributions. The number of covariates and the distribution of sojourn times can be specified for each possible transition providing a great flexibility in a model’s definition. This article presents parametric semi-Markov models and gives a detailed description of the package together with an application to asthma control.

Suggested Citation

  • Król, Agnieszka & Saint-Pierre, Philippe, 2015. "SemiMarkov: An R Package for Parametric Estimation in Multi-State Semi-Markov Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 66(i06).
    • Handle: RePEc:jss:jstsof:v:066:i06
    DOI: http://hdl.handle.net/10.18637/jss.v066.i06
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    References listed on IDEAS

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    1. Brahim Ouhbi & Nikolaos Limnios, 1999. "Nonparametric Estimation for Semi-Markov Processes Based on its Hazard Rate Functions," Statistical Inference for Stochastic Processes, Springer, vol. 2(2), pages 151-173, May.
    2. Jackson, Christopher, 2011. "Multi-State Models for Panel Data: The msm Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 38(i08).
    3. de Wreede, Liesbeth C. & Fiocco, Marta & Putter, Hein, 2011. "mstate: An R Package for the Analysis of Competing Risks and Multi-State Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 38(i07).
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    Cited by:

    1. Guibert, Quentin & Planchet, Frédéric, 2018. "Non-parametric inference of transition probabilities based on Aalen–Johansen integral estimators for acyclic multi-state models: application to LTC insurance," Insurance: Mathematics and Economics, Elsevier, vol. 82(C), pages 21-36.
    2. Fuino, Michel & Wagner, Joël, 2018. "Long-term care models and dependence probability tables by acuity level: New empirical evidence from Switzerland," Insurance: Mathematics and Economics, Elsevier, vol. 81(C), pages 51-70.
    3. Hongwen Guo & Mo Zhang & Paul Deane & Randy E. Bennett, 2019. "Writing Process Differences in Subgroups Reflected in Keystroke Logs," Journal of Educational and Behavioral Statistics, , vol. 44(5), pages 571-596, October.
    4. Kung-Jeng Wang & Chia-Min Lee & Gwo-Chi Hu & Kung-Min Wang, 2020. "Stroke to Dementia Associated with Environmental Risks—A Semi-Markov Model," IJERPH, MDPI, vol. 17(6), pages 1-13, March.
    5. Guglielmo D'Amico & Stefania Scocchera & Loriano Storchi, 2021. "Randentropy: a software to measure inequality in random systems," Papers 2103.09107, arXiv.org.

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