presmoothedTP

Multi-state models are essential tools in longitudinal data analysis. One primary goal of these models is the estimation of transition probabilities, a critical metric for predicting clinical prognosis across various stages of diseases or medical conditions. Traditionally, inference in multi-state models relies on the Aalen-Johansen (AJ) estimator which is consistent under the Markov assumption. However, in many practical applications, the Markovian nature of the process is often not guaranteed, limiting the applicability of the AJ estimator in more complex scenarios. This package extends the landmark Aalen-Johansen estimator Putter, H, Spitoni, C (2018) incorporating presmoothing techniques described by Soutinho, Meira-Machado and Oliveira (2020) doi:10.1080/03610918.2020.1762895, offering a robust alternative for estimating transition probabilities in non-Markovian multi-state models with multiple states and potential reversible transitions.

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