Multistate Models: A Review
 Philip Hougaard
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Multistate models are models for a process, for example describing a life history of an individual, which at any time occupies one of a few possible states. This can describe several possible events for a single individual, or the dependence between several individuals. The events are the transitions between the states. This class of models allows for an extremely flexible approach that can model almost any kind of longitudinal failure time data. This is particularly relevant for modeling different events, which have an eventrelated dependence, like occurrence of disease changing the risk of death. It can also model paired data. It is useful for recurrent events, but has limitations. The Markov models stand out as much simpler than other models from a probability point of view, and this simplifies the likelihood evaluation. However, in many cases, the Markov models do not fit satisfactorily, and happily, it is reasonably simple to study nonMarkov models, in particular the Markov extension models. This also makes it possible to consider, whether the dependence is of shortterm or longterm nature. Applications include the effect of heart transplantation on the mortality and the mortality among Danish twins.
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 Title
 Multistate Models: A Review
 Journal

Lifetime Data Analysis
Volume 5, Issue 3 , pp 239264
 Cover Date
 19990901
 DOI
 10.1023/A:1009672031531
 Print ISSN
 13807870
 Online ISSN
 15729249
 Publisher
 Kluwer Academic Publishers
 Additional Links
 Topics
 Keywords

 bivariate survival
 dependence
 disability model
 Markov models
 recurrent events
 survival data
 Industry Sectors
 Authors

 Philip Hougaard ^{(1)}
 Author Affiliations

 1. Novo Nordisk, Novo Alle Bldg, 9F2, DK2880, Bagsvaerd, Denmark