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 …

Panel data are observations of a continuous-time process at arbitrary times, for example,
visits to a hospital to diagnose disease status. Multi-state models for such data are generally …

Multi-state 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 …

Continuous-time multistate models are widely used for categorical response data,
particularly in the modeling of chronic diseases. However, inference is difficult when the …

Many chronic diseases have a natural interpretation in terms of staged progression.
Multistate models based on Markov processes are a well-established method of estimating …

Multi‐state Markov models can be useful in analysing disease history data. We apply the
general estimation methods of Kalbfleisch and Lawless to panel data in which individuals …

Many chronic medical conditions can be meaningfully characterized in terms of a two-state
stochastic process. Here we consider the problem in which subjects make transitions among …

Multi-state models are a popular method of describing medical processes that can be
represented as discrete states or stages. They have particular use when the data are panel …

Markov transition models are frequently used to model disease progression. The authors
show how the solution to Kolmogorov's forward equations can be exploited to map between …

Clustered progressive chronic disease processes arise when interest lies in modeling
damage in paired organ systems (eg, kidneys, eyes), in diseases manifest in different organ …