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  • Inference for transition probabilities in non-Markov multi-state models
    Andersen, Per Kragh ; Sparre Wandall, Eva Nina ; Pohar Perme, Maja
    Multi-state models are frequently used when data come from subjects observed over time and where focus is on the occurrence of events that the subjects may experience. A convenient modeling ... assumption is that the multi-state stochastic process is Marko- vian, in which case a number of methods are available when doing inference for both transition intensities and transition probabilities. The Markov assumption, however, is quite strict and may not fit actual data in a satisfactory way. Therefore, inference methods for non-Markov models are needed. In this paper, we review methods for estimating transition probabilities in such models and suggest ways of doing regres- sion analysis based on pseudo observations. In particular, we will compare methods using land-marking with methods using plug-in. The methods are illustrated using simulations and practical examples from medical research.
    Vir: Lifetime data analysis. - ISSN 1380-7870 (iss. 4, Vol. 28, Oct. 2022, str. 585-604)
    Vrsta gradiva - članek, sestavni del
    Leto - 2022
    Jezik - angleški
    COBISS.SI-ID - 117897475

    Povezava(-e):

    https://link.springer.com/content/pdf/10.1007/s10985-022-09560-w.pdf
    DOI

vir: Lifetime data analysis. - ISSN 1380-7870 (iss. 4, Vol. 28, Oct. 2022, str. 585-604)
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