Introduction
Hidden Markov models (HMM) have previously been used to describe disease dynamics characterized by a set of discrete states (1). These models consist of an observable longitudinal process and an unobservable latent state process modelled as a discrete-time Markov chain. Inference of these models involves both latent state probability estimates, and maximum likelihood estimates of the model parameters. One possible application of HMMs is in respiratory diseases, such as asthma and chronic obstructive pulmonary disease, in which the risk of having an exacerbation, an acute worsening event, is a primary endpoint in clinical trials. In general, the incidence of exacerbations is low, leading to long clinical trials. A composite event endpoint, CompEx (2), has therefore been developed to reduce the necessary clinical trial duration by making use of home-measured spirometry data, such as peak expiratory flow (PEF ). An alternate approach to this is to model PEF with worsening events of varying magnitudes included explicitly in a dynamic model. This can be done using an HMM. In this work, a simulation study was conducted to ensure unbiased estimates across several simulation scenarios.