Joint modelling of longitudinal and multi-state processes: application to clinical progressions in prostate cancer

Author:

Ferrer Loïc1,Rondeau Virginie1,Dignam James2,Pickles Tom3,Jacqmin-Gadda Hélène1,Proust-Lima Cécile1

Affiliation:

1. INSERM U1219, ISPED; Université de Bordeaux; Bordeaux France

2. Department of Public Health Sciences; University of Chicago and NRG Oncology; Chicago IL U.S.A.

3. Department of Radiation Oncology; University of British Columbia; Vancouver Canada

Publisher

Wiley

Subject

Statistics and Probability,Epidemiology

Reference34 articles.

1. Cox's regression model for counting processes: a large sample study;Andersen;The Annals of Statistics,1982

2. Time-dependent covariates in the Cox proportional-hazards regression model;Fisher;Annual Review of Public Health,1999

3. Simultaneously modelling censored survival data and repeatedly measured covariates: a Gibbs sampling approach;Faucett;Statistics in Medicine,1996

4. A joint model for survival and longitudinal data measured with error;Wulfsohn;Biometrics,1997

5. Joint modeling of longitudinal and time-to-event data: an overview;Tsiatis;Statistica Sinica,2004

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