Non-homogeneous Markov processes for biomedical data analysis.
Some time ago, the Markov processes were introduced in biomedical sciences in order to study disease history events. Homogeneous and Non-homogeneous Markov processes are an important field of research into stochastic processes, especially when exact transition times are unknown and interval-censored observations are present in the analysis. Non-homogeneous Markov process should be used when the homogeneous assumption is too strong. However these sorts of models increase the complexity of the analysis and standard software is limited. In this paper, some methods for fitting non-homogeneous Markov models are reviewed and an algorithm is proposed for biomedical data analysis. The method has been applied to analyse breast cancer data. Specific software for this purpose has been implemented.
Ocaña-Riola
Ricardo
R
Escuela Andaluza de Salud Pública, Campus Universitario de Cartuja, Cuesta del Observatorio, 4, Apdo. de Correos 2070, 18080 Granada, Spain. ricardo.ocana.easp@juntadeandalucia.es
D000465:Algorithms; D001698:Biomedical Engineering / Q000379:methods; D001699:Biometry / Q000379:methods*; D001943:Breast Neoplasms / Q000175:diagnosis / Q000188:drug therapy* / Q000401:mortality*; D003198:Computer Simulation; D006801:Humans; D008390:Markov Chains; D008954:Models, Biological; D015233:Models, Statistical; D017711:Nonlinear Dynamics; D018570:Risk Assessment / Q000379:methods*; D012307:Risk Factors; D016019:Survival Analysis; D016896:Treatment Outcome