  Composite endpoints that combine multiple outcomes on different scales are
common in clinical trials, particularly in chronic conditions. In many of these
cases, patients will have to cross a predefined responder threshold in each of
the outcomes to be classed as a responder overall. One instance of this occurs
in systemic lupus erythematosus (SLE), where the responder endpoint combines
two continuous, one ordinal and one binary measure. The overall binary
responder endpoint is typically analysed using logistic regression, resulting
in a substantial loss of information. We propose a latent variable model for
the SLE endpoint, which assumes that the discrete outcomes are manifestations
of latent continuous measures and can proceed to jointly model the components
of the composite. We perform a simulation study and find the method to offer
large efficiency gains over the standard analysis. We find that the magnitude
of the precision gains are highly dependent on which components are driving
response. Bias is introduced when joint normality assumptions are not
satisfied, which we correct for using a bootstrap procedure. The method is
applied to the Phase IIb MUSE trial in patients with moderate to severe SLE. We
show that it estimates the treatment effect 2.5 times more precisely, offering
a 60% reduction in required sample size.
