Transition Models in Pharmacodynamics

Markov models have been used to describe disease as a series of probable transitions between health states. The method has considerable appeal for use in pharmacometrics since it offers an approach to evaluate patient compliance with prescribed medication regimen, multiple health-states simultaneously, and transitions between different sleep stages. An overview of the Markov model has been provided together with the Markovian assumption. The most commonly used form of the Markov model, the discrete time Markov model, has been described as well as its application in the mixed-effects modeling setting. The chapter concludes with a discussion of a hybrid Markov mixed-effects and proportional odds models used to characterize an adverse effect that lends itself to this combination modeling approach.