title: The versatility of multi-state models for the analysis of longitudinal data with unobservable features.
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.
authors.affiliation: MRC Biostatistics Unit, Institute of Public Health, Robinson Way, Cambridge, CB2 0SR, UK, vern.farewell@mrc-bsu.cam.ac.uk.
authors.forename: Vernon T
authors.initials: VT
authors.lastname: Farewell
authors.forename: Brian D M
authors.initials: BD
authors.lastname: Tom
mesh_terms: D000328:Adult; D015535:Arthritis, Psoriatic / Q000503:physiopathology / Q000523:psychology; D003327:Coronary Disease / Q000453:epidemiology; D005260:Female; D006801:Humans; D016013:Likelihood Functions; D008137:Longitudinal Studies / Q000379:methods*; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D011788:Quality of Life / Q000523:psychology; D016019:Survival Analysis