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In this paper we consider nonparametric estimation of transition probabilities for multi-state models. Specifically, we focus on the illness-death or disability model. The main novelty of the proposed estimators is that they do not rely on the Markov assumption, typically assumed to hold in a multi-state model. We investigate the asymptotic properties of(More)
The Cox proportional hazards regression model has become the traditional choice for modeling survival data in medical studies. To introduce flexibility into the Cox model, several smoothing methods may be applied, and approaches based on splines are the most frequently considered in this context. To better understand the effects that each continuous(More)
The aim of this paper is to present an R library, called tdc.msm, developed to analyze multi-state survival data. In this library, the time-dependent regression model and multi-state models are included as two possible approaches for such data. For the multi-state modelling five different models are considered, allowing the user to choose between Markov and(More)
Let (T 1 , T 2) be gap times corresponding to two consecutive events, which are observed subject to random right-censoring, and suppose the vector (T 1 , T 2) satisfies the nonparametric location-scale regression model T 2 = m(T 1) + σ(T 1)ε, where the functions m and σ are 'smooth', and ε is independent of T 1. The aim of this paper is twofold. First, we(More)
One major goal in clinical applications of multi-state models is the estimation of transition probabilities. The usual nonparametric estimator of the transition matrix for non-homogeneous Markov processes is the Aalen-Johansen estimator (Aalen and Johansen [1]). However, two problems may arise from using this estimator: first, its standard error may be(More)
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