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 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 random-effects disease progression model (DPM), using minimally informative prior distributions, for FVC trajectory and hazard for 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 (PROFILE and INJUSTIS). Results: The DPM yields a higher estimated rate of FVC decline (6.0 vs 4.7%/year), a more precise fit than a mixed linear model of FVC alone, and replicates the non-linear pattern in observed data. By modelling the full FVC trajectory rather than only the change from baseline at a given time point, the approach increases the information obtained 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.
Wendelberger et al. (Sun,) studied this question.