A statistical model for lung function trajectory and mortality in patients with fibrotic interstitial lung disease
Author(s)
Jenkins, Gisli
Type
Journal Article
Abstract
Background. Fibrotic interstitial lung diseases (ILDs) cause loss of forced vital capacity (FVC) and increased risk of death over time. Most clinical trials aim to slow FVC decline and reduce mortality. However, the association of lower FVC with higher mortality will bias simple estimates of differences in FVC progression between groups. Therefore, both the time-dependent decline in FVC and increase in mortality should be jointly modelled.
Methods. We developed a Bayesian, joint mixed effects disease progression model (DPM), using minimally informative prior distributions, for FVC trajectory and the hazard for ILD-related mortality over time. This model minimizes bias due to mortality in estimating differences in the rate of FVC decline and is suitable for use when characterizing populations or in estimating a treatment effect in a clinical trial. The DPM was applied to individual patient data from prospective cohort studies of fibrotic ILD.
Results. The DPM yields a higher estimated rate of FVC decline (6.0 vs 4.7%/year) and a more precise fit than a linear mixed model of FVC alone, and replicates the non-linear pattern in the observed data. By modelling the full FVC trajectory rather than only the change from baseline at a given time point, the DPM increases the information from each patient and reduces both the time to information and the effect of variability in baseline FVC measurements on the estimation of treatment effects.
Conclusions. The joint DPM provides an integrated approach to minimizing bias in the estimation of treatment effects in clinical trials in fibrotic ILDs.
Methods. We developed a Bayesian, joint mixed effects disease progression model (DPM), using minimally informative prior distributions, for FVC trajectory and the hazard for ILD-related mortality over time. This model minimizes bias due to mortality in estimating differences in the rate of FVC decline and is suitable for use when characterizing populations or in estimating a treatment effect in a clinical trial. The DPM was applied to individual patient data from prospective cohort studies of fibrotic ILD.
Results. The DPM yields a higher estimated rate of FVC decline (6.0 vs 4.7%/year) and a more precise fit than a linear mixed model of FVC alone, and replicates the non-linear pattern in the observed data. By modelling the full FVC trajectory rather than only the change from baseline at a given time point, the DPM increases the information from each patient and reduces both the time to information and the effect of variability in baseline FVC measurements on the estimation of treatment effects.
Conclusions. The joint DPM provides an integrated approach to minimizing bias in the estimation of treatment effects in clinical trials in fibrotic ILDs.
Date Issued
2026-04-07
Date Acceptance
2026-01-04
Citation
American Journal of Respiratory and Critical Care Medicine, 2026
ISSN
1073-449X
Publisher
American Thoracic Society
Journal / Book Title
American Journal of Respiratory and Critical Care Medicine
Copyright Statement
© The Author(s) 2026. Published by Oxford University Press on behalf of the American Thoracic Society. This is the author’s accepted manuscript made available under a CC-BY licence in accordance with Imperial’s Research Publications Open Access policy (www.imperial.ac.uk/oa-policy)
License URL
Publication Status
Accepted
Article Number
aamag103
Date Publish Online
2026-04-07