Abstract
Lung function of people with asthma fluctuates substantially over time, and increased variability has been linked to elevated risk of worsening events called exacerbations [1,2]. Although home-measured peak expiratory flow (PEF) can provide richer temporal information than in-clinic measurements, its irregular dynamics makes it difficult to analyse using conventional modelling approaches. We explore a modelling strategy that treats PEF patterns as transitions between latent disease states, using a mixed-effects hidden Markov model (MEHMM) [3] to capture both between-patient heterogeneity and state-dependent changes in disease severity. Parameter estimation relies on a modified version [4] of the stochastic approximation expectation–maximization (SAEM) algorithm, adapted for hidden Markov models. This approach aims to extract treatment effects directly from underlying state dynamics, and we evaluate its feasibility and robustness through a simulation study as well as its application to phase 2b clinical trial data.