Joint modelling
death
EVENTS, EVENT
We used joint models for longitudinal and time-to-event data. These comprise two sub-models for the respective processes and an association structure to connect them. We assessed the association between change in weight (kg) and three time-to-event endpoints (time to acute exacerbation of ILD or death, time to ILD progression, time to ILD progression or death) over 52 weeks and over the whole trial. In the longitudinal sub-model, a normal mixed effects model of weight was used, with HRCT pattern (UIP-like fibrotic pattern or other fibrotic patterns) and weight at baseline as predictor variables. Separate mean slopes for subjects in the nintedanib and placebo groups were assumed. Trajectories were modelled by a linear trend with an unstructured variance–covariance matrix assumed. Weight was assessed at baseline, at weeks 2, 4, 6, 12, 24, 36 and 52 and every 16 weeks thereafter. Only values obtained before an event or censoring timepoint were considered. In the time-to-event sub‑model, a piecewise exponential model with five knots was used to model the baseline hazard. Weight was used as the endogenous time-dependent covariate and treatment as a predictor variable. The sub-model was stratified by HRCT pattern. Subjects were censored once they experienced an event and were not considered at risk of further events.The shared parameter in each of the joint models was the estimated slope of weight (i.e., the annual rate of change in weight), which assumed that the rate of change in weight affected the risk of an event. Joint models were fitted for each time-to-event endpoint. We present the risk of the first event in terms of 1-unit, 4-unit and 10-unit decreases in weight (kg). The joint model approach was implemented with the SAS macro %JM [