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

Multistate models characterize disease processes within an individual. Clinical studies often
observe the disease status of individuals at discrete time points, making exact times of …

In a clinical trial of a treatment for alcoholism, the usual response variable of interest is the
number of alcoholic drinks consumed by each subject each day. Subjects in these trials are …

In this article, we propose a graphical technique for assessing the goodness-of-fit of a
stationary hidden Markov model (HMM). We show that plots of the estimated distribution …

Multistate models are used to characterize individuals' natural histories through diseases
with discrete states. Observational data resources based on electronic medical records pose …

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 …

In this paper we present a formal treatment of non‐homogeneous Markov chains by
introducing a hierarchical Bayesian framework. Our work is motivated by the analysis of …

Multiple longitudinal responses are often collected as a means to capture relevant features
of the true outcome of interest, which is often hidden and not directly measurable. We outline …

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 …

We estimate Markov transition probabilities for individual health status over time as function
of observable characteristics. We implement 3 methods to construct these Markov …