markovMSM: An R Package for Checking the Markov Condition in Multi-State Survival Data

Abstract:

Multi-state models can be used to describe processes in which an individual moves through a finite number of states in continuous time. These models allow a detailed view of the evolution or recovery of the process and can be used to study the effect of a vector of explanatory variables on the transition intensities or to obtain prediction probabilities of future events after a given event history. In both cases, before using these models, we have to evaluate whether the Markov assumption is tenable. This paper introduces the markovMSM package, a software application for R, which considers tests of the Markov assumption that are applicable to general multi-state models. Three approaches using existing methodology are considered: a simple method based on including covariates depending on the history; methods based on measuring the discrepancy of the non-Markov estimators of the transition probabilities to the Markovian Aalen-Johansen estimators; and, finally, methods that were developed by considering summaries from families of log-rank statistics where individuals are grouped by the state occupied by the process at a particular time point. The main functionalities of the markovMSM package are illustrated using real data examples.

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Published

Sept. 24, 2023

Received

May 20, 2022

DOI

10.32614/RJ-2023-032

Volume

Pages

15/1

195 - 211


Supplementary materials

Supplementary materials are available in addition to this article. It can be downloaded at RJ-2023-032.zip

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    Citation

    For attribution, please cite this work as

    Soutinho & Meira-Machado, "markovMSM: An R Package for Checking the Markov Condition in Multi-State Survival Data", The R Journal, 2023

    BibTeX citation

    @article{RJ-2023-032,
      author = {Soutinho, Gustavo and Meira-Machado, Luís},
      title = {markovMSM: An R Package for Checking the Markov Condition in Multi-State Survival Data},
      journal = {The R Journal},
      year = {2023},
      note = {https://doi.org/10.32614/RJ-2023-032},
      doi = {10.32614/RJ-2023-032},
      volume = {15},
      issue = {1},
      issn = {2073-4859},
      pages = {195-211}
    }