Inference for transition probabilities in non-Markov multi-state models

Per Kragh Andersen (), Eva Nina Sparre Wandall and Maja Pohar Perme
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Per Kragh Andersen: University of Copenhagen
Eva Nina Sparre Wandall: University of Copenhagen
Maja Pohar Perme: University of Ljubljana

Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, 2022, vol. 28, issue 4, No 4, 585-604

Abstract: Abstract 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 Markovian, 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 regression 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.

Keywords: Land-marking; Markov process; Multi-state model; Non-Markov model; Plug-in; Pseudo observations; State occupation probability; Survival analysis; Transition intensity; Transition probability (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s10985-022-09560-w

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