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Multi-state models are increasingly being used to model complexepidemiological and clinical outcomes over time. It is common to assume thatthe models are Markov, but the assumption can often be unrealistic. The Markovassumption is seldomly checked and violations can lead to biased estimation formany parameters of interest. As argued by Datta and Satten (2001), theAalen-Johansen estimator of occupation probabilities is consistent also in thenon-Markov case. Putter and Spitoni (2018) exploit this fact to construct aconsistent estimator of state transition probabilities, the landmarkAalen-Johansen estimator, which does not rely on the Markov assumption. Adisadvantage of landmarking is data reduction, leading to a loss of power. Thisis problematic for less traveled transitions, and undesirable when suchtransitions indeed exhibit Markov behaviour. Using a framework of partiallynon-Markov multi-state models we suggest a hybrid landmark Aalen-Johansenestimator for transition probabilities. The proposed estimator is a compromisebetween regular Aalen-Johansen and landmark estimation, using transitionspecific landmarking, and can drastically improve statistical power. Themethods are compared in a simulation study and in a real data applicationmodelling individual transitions between states of sick leave, disability,education, work and unemployment. In the application, a birth cohort of 184951Norwegian men are followed for 14 years from the year they turn 21, using datafrom national registries.

A hybrid landmark Aalen-Johansen estimator for transition probabilities in partially non-Markov multi-state models