Parameter estimation in a model for misclassified Markov data - a Bayesian approach.

Rosychuk and Islam have a paper in Computational Statistics and Data Analysis. This concerns parameter estimation in a two-state recurrent misclassification type hidden Markov model, where the Markov process is assumed to be continuous time and in equilibrium and is observed at discrete, equally spaced time points. A Bayesian approach to estimation is considered via Gibbs sampling. To avoid identifiability issues, the misclassification probabilities are constrained to be below 0.5. An additional issue is the choice of starting values of the transition probabilities for the latent Markov process. Values based on simple correction formulae previously developed by Rosychuk and Thompson appear to perform better than values based on taking naive estimates of the transition probabilities of the observed process.

Competing risks and time-dependent covariates

Cortese and Andersen have a paper available as a research report from the Department of Biostatistics, University of Copenhagen. This concerns the problem of prediction in competing risks models where there are internal time-dependent covariates, meaning the trajectory of the covariate is not predictable, nor is it independent of the development of the disease/mortality process. The authors focus on the case where the time-dependent covariate is binary, and once it has value 1, cannot revert to value 0. They investigate three approaches. The first expands the state space of the competing risks model, having two alive states: 'alive and cov=0' and 'alive and cov=1' and applies standard methods based on Nelson-Aalen and Aalen-Johansen estimators. The two other methods are based on landmarking.

Update: This paper is now published in Biometrical Journal.