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Modeling Peak Expiratory Flow in Patients With Asthma and Quantifying Treatment Effects Using a Mixed-Effects Hidden
Ludvig Jakobsson1,2,3, Marcus Baaz2, Jacob Leander3
1Department of Mathematical Sciences, Chalmers University of Technology and University of Gothenburg, Gothenburg, Sweden.
CPT: Pharmacometrics & Systems Pharmacology
|June 6, 2026
Summary
This study introduces a new statistical model for analyzing home-measured peak expiratory flow (PEF) in asthma patients. The model helps shorten clinical trials by better utilizing PEF data.
Area of Science:
- Respiratory Medicine
- Biostatistics
- Clinical Trial Design
Background:
- Exacerbations are common primary endpoints in asthma and COPD clinical trials.
- Low exacerbation frequency leads to prolonged and expensive trials.
- Home-measured spirometry offers a potential solution to shorten trial durations.
Purpose of the Study:
- To develop a novel statistical model for analyzing home-measured peak expiratory flow (PEF).
- To assess the utility of a mixed-effects hidden Markov model (MEHMM) for home-based PEF data.
- To investigate dose-response relationships in asthma treatment using this model.
Main Methods:
- Development of a mixed-effects hidden Markov model (MEHMM).
- Incorporation of a two-state latent disease process (high vs. low PEF).
- Implementation of an inference framework for parameter estimation and uncertainty quantification.
Main Results:
- Reliable estimation of model parameters was demonstrated.
- Statistically significant treatment effects on multiple model components were identified.
- The model successfully analyzed data from a velsecorat phase 2b asthma study.
Conclusions:
- Latent disease-state models can effectively extract information from home-measured spirometry.
- The MEHMM provides a robust method for analyzing PEF data in clinical trials.
- This approach supports more efficient clinical trial designs for respiratory diseases.
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