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 …

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 …

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

The focus of this article is on Markov models for the analysis of panel data and, more
specifically, on data obtained from repeated measurements of one categorical variable at …

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 …

Medical researchers interested in temporal, multivariate measurements of complex diseases
have recently begun developing health state models, which divide the space of patient …

Markov chain models are frequently used for studying event histories that include transitions
between several states. An empirical transition matrix for nonhomogeneous Markov chains …

Suppose that a heterogeneous group of individuals is followed over time and that each
individual can be in state 0 or state 1 at each time point. The sequence of states is assumed …

Methods for the analysis of panel data under a continuous-time Markov model are proposed.
We present procedures for obtaining maximum likelihood estimates and associated …