Many applications in medical statistics and other fields can be described by transitions between multiple states (e.g. from health to disease) experienced by individuals over time. In this context, multi-state models are a popular statistical technique, in particular when the exact transition times are not observed. The key quantities of interest are the transition rates, capturing the instantaneous risk of moving from one state to another. The main contribution of this work is to propose a joint semiparametric model for several possibly related multi-state processes (Seemingly Unrelated Multi-State, SUMS, processes), assuming a Markov structure for the transitions over time. The dependence between different processes is captured by specifying a joint prior distribution on the transition rates of each process. In this case, we assume a flexible distribution, which allows for clustering of the individuals, overdispersion and outliers. Moreover, we employ a graph structure to describe the dependence among processes, exploiting tools from the Gaussian Graphical model literature. It is also possible to include covariate effects. We use our approach to model disease progression in mental health. Posterior inference is performed through a specially devised MCMC algorithm.

(2022). Seemingly Unrelated Multi-State Processes: A Bayesian Semiparametric Approach [journal article - articolo]. In BAYESIAN ANALYSIS. Retrieved from https://hdl.handle.net/10446/233885

Seemingly Unrelated Multi-State Processes: A Bayesian Semiparametric Approach

Argiento, Raffaele;
2022-01-01

Abstract

Many applications in medical statistics and other fields can be described by transitions between multiple states (e.g. from health to disease) experienced by individuals over time. In this context, multi-state models are a popular statistical technique, in particular when the exact transition times are not observed. The key quantities of interest are the transition rates, capturing the instantaneous risk of moving from one state to another. The main contribution of this work is to propose a joint semiparametric model for several possibly related multi-state processes (Seemingly Unrelated Multi-State, SUMS, processes), assuming a Markov structure for the transitions over time. The dependence between different processes is captured by specifying a joint prior distribution on the transition rates of each process. In this case, we assume a flexible distribution, which allows for clustering of the individuals, overdispersion and outliers. Moreover, we employ a graph structure to describe the dependence among processes, exploiting tools from the Gaussian Graphical model literature. It is also possible to include covariate effects. We use our approach to model disease progression in mental health. Posterior inference is performed through a specially devised MCMC algorithm.
articolo
Cremaschi, Andrea; Argiento, Raffaele; De Iorio, Maria; Shirong, Cai; Chong, Yap Seng; Meaney, Michael; Kee, Michelle
(2022). Seemingly Unrelated Multi-State Processes: A Bayesian Semiparametric Approach [journal article - articolo]. In BAYESIAN ANALYSIS. Retrieved from https://hdl.handle.net/10446/233885
File allegato/i alla scheda:
File Dimensione del file Formato  
Bayesian_Analysis_2022_con_Andrea_e_Maria.pdf

accesso aperto

Versione: publisher's version - versione editoriale
Licenza: Creative commons
Dimensione del file 401.43 kB
Formato Adobe PDF
401.43 kB Adobe PDF Visualizza/Apri
Pubblicazioni consigliate

Aisberg ©2008 Servizi bibliotecari, Università degli studi di Bergamo | Terms of use/Condizioni di utilizzo

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10446/233885
Citazioni
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact