@misc{10481/69357,
year = {2019},
month = {12},
url = {http://hdl.handle.net/10481/69357},
abstract = {Multi-state models are considered in the field of survival analysis for modelling
illnesses that evolve through several stages over time. Multi-state models can be
developed by applying several techniques, such as non-parametric, semi-parametric
and stochastic processes, particularly Markov processes. When the development of
an illness is being analysed, its progression is tracked periodically. Medical reviews
take place at discrete times, and a panel data analysis can be formed. In this paper, a
discrete-time piecewise non-homogeneous Markov process is constructed for
modelling and analysing a multi-state illness with a general number of states. The
model is built, and relevant measures, such as survival function, transition probabilities, mean total times spent in a group of states and the conditional probability of
state change, are determined. A likelihood function is built to estimate the parameters and the general number of cut-points included in the model. Time-dependent
covariates are introduced, the results are obtained in a matrix algebraic form and the
algorithms are shown. The model is applied to analyse the behaviour of breast
cancer. A study of the relapse and survival times of 300 breast cancer patients who
have undergone mastectomy is developed. The results of this paper are implemented
computationally with MATLAB and R.},
organization = {Ministerio de Economía y Competitividad FQM-307},
organization = {European Regional Development Fund (ERDF) MTM2017-88708-P},
organization = {University of Milano-Bicocca 2014-ATE-0228},
publisher = {Springer},
keywords = {Survival},
keywords = {Breast Cancer},
keywords = {Piecewise Markov model},
keywords = {Multi-state model},
title = {A general piecewise multi-state survival model: Application to breast cancer},
doi = {https://doi.org/10.1007/s10260-019-00505-6},
author = {Ruiz Castro, Juan Eloy and Zenga, Mariangela},
}