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The versatility of multi-state models for the analysis of longitudinal data with unobservable features.

Published version
Peer-reviewed

Type

Article

Change log

Authors

Farewell, Vernon T 
Tom, Brian DM 

Abstract

Multi-state models provide a convenient statistical framework for a wide variety of medical applications characterized by multiple events and longitudinal data. We illustrate this through four examples. The potential value of the incorporation of unobserved or partially observed states is highlighted. In addition, joint modelling of multiple processes is illustrated with application to potentially informative loss to follow-up, mis-measured or missclassified data and causal inference.

Description

Keywords

Adult, Arthritis, Psoriatic, Coronary Disease, Female, Humans, Likelihood Functions, Longitudinal Studies, Male, Middle Aged, Models, Statistical, Quality of Life, Survival Analysis

Journal Title

Lifetime Data Anal

Conference Name

Journal ISSN

1380-7870
1572-9249

Volume Title

20

Publisher

Springer Science and Business Media LLC
Sponsorship
MRC (unknown)