Non-Homogeneous Markov Models in the Analysis of Survival After Breast Cancer

DOI10.1111/1467-9876.00223zbMath1021.62096OpenAlexW2067173654MaRDI QIDQ4422388

Rafael Pérez-Ocón, Juan Eloy Ruiz-Castro, María Luz Gámiz Pérez

Publication date: 20 October 2003

Published in: Journal of the Royal Statistical Society Series C: Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/1467-9876.00223

zbMATH Keywords

survival datacovariatesWeibull distributionmaximum likelihood estimatenon-homogeneous Markov process

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Applications of Markov chains and discrete-time Markov processes on general state spaces (social mobility, learning theory, industrial processes, etc.) (60J20) Estimation in survival analysis and censored data (62N02)

Related Items

A Semi-Markov Model in Biomedical Studies ⋮ Bayesian assessment of times to diagnosis in breast cancer screening ⋮ Reliability of manufacturing equipment in~complex~environments ⋮ A correlated random effects model for non-homogeneous Markov processes with nonignorable missingness ⋮ Nonparametric estimation in an illness‐death model with component‐wise censoring ⋮ Evaluation of the natural history of disease by combining incident and prevalent cohorts: application to the Nun study ⋮ Modeling Nonhomogeneous Markov Processes via Time Transformation ⋮ Non-homogeneous Markov process models with informative observations with an application to Alzheimer's disease ⋮ Methods for checking the Markov condition in multi-state survival data ⋮ Modelling of overall survival by an association between progression-free and post-progression survival using a conditional distribution