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 …

SUMMARY A Markov chain is a useful way of describing cohort data Longitudinal
observations of a marker of the progression of the human immunodeficiency virus (HIV) …

We consider the estimation of the intensity and survival functions for a continuous time
progressive three-state semi-Markov model with intermittently observed data. The estimator …

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 …

Multistate models have been increasingly used to model natural history of many diseases as
well as to characterize the follow-up of patients under varied clinical protocols. This …

The semiparametric maximum likelihood estimation is considered in a three-state duration
dependent Markov process when times of the intermediate transition (eg, onset of a disease) …

In many chronic diseases it is important to understand the rate at which patients progress
from infection through a series of defined disease states to a clinical outcome, eg cirrhosis in …

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

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 …

Back‐calculation is a widely used method to estimate HIV incidence rates, and is commonly
based on times of AIDS diagnosis. Following up earlier work, we extend this method to also …