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 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 …

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 …

Disease progression in prospective clinical and epidemiological studies is often
conceptualized in terms of transitions between disease states. Analysis of data from such …

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

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 …

We consider the analysis of serial biomarkers to screen and monitor individuals in a given
population for onset of a specific disease of interest. The biomarker readings are subject to …

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

Non-homogeneous Markov process models for life history data are studied. We review
Aalen & Johansen's (1978) methodology for estimating transition probabilities in such …