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• W2184753479 abstract "The multi-state Markov model is a useful way of describing a process in which an individual moves through a series of states in continuous time. The msm package for R allows a general multi-state model to be fitted to longitudinal data. Data often consist of observations of the process at arbitrary times, so that the exact times when the state changes are unobserved. For example, the progression of chronic diseases is often described by stages of severity, and the state of the patient may only be known at doctor or hospital visits. Features of msm include the ability to model transition rates and hidden Markov output models in terms of covariates, and the ability to model data with a variety of observation schemes, including censored states. Hidden Markov models, in which the true path through states is only observed through some error-prone marker, can also be fitted. The observation is generated, conditionally on the underlying states, via some distribution. An example is a screening misclassification model in which states are observed with error. More generally, hidden Markov models can have a continuous response, with some arbitrary distribution, conditionally on the underlying state. This manual introduces the theory behind multi-state Markov and hidden Markov models, and gives a tutorial in the typical use of the msm package, illustrated by some typical applications to modelling chronic diseases. Much of the material in this manual is published, in a more concise form, in Journal of Statistical Software (2011) 38(8):1-29, http://www.jstatsoft.org/v38/i08/" @default.
• W2184753479 created "2016-06-24" @default.
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• W2184753479 date "2014-01-01" @default.
• W2184753479 modified "2023-09-23" @default.
• W2184753479 title "Multi-state modelling with R: the msm package" @default.
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