Composite endpoints combine a number of outcomes to assess the efficacy of a treatment.
They are used in situations where it is difficult to identify a single relevant endpoint,
such as in complex multisystem diseases. Our focus in this thesis is on composite
responder endpoints, which allocate patients as either ‘responders’ or ‘non-responders’
based on whether they cross predefined thresholds in the individual outcomes. These
composites are often combinations of continuous and discrete measures and are typically
collapsed into a single binary endpoint and analysed using logistic regression. However,
this is at the expense of losing information on how close each patient was to the responder
threshold. As well as being inefficient the analysis is sensitive to misclassification
due to measurement error. The augmented binary method was introduced to improve
the analysis of composite responder endpoints comprised of a single continuous and
binary endpoint, by making use of the continuous information.
In this thesis we build on this work to address some of the existing limitations. We
implement small sample corrections for the standard binary and augmented binary
methods and assess the performance for application in rare disease trials, where the
gains are most needed. We find that employing the small sample corrected augmented
binary method results in a reduction of required sample size of 32%. Motivated by
systemic lupus erythematosus (SLE), we consider the case where the composite has
multiple continuous, ordinal and binary components. We adapt latent variable models
for application to these endpoints and assess the performance in simulated data and
phase IIb trial data in SLE. Our findings show reductions in required sample size of at
least 60%, however the magnitude of the gains depends on which components drive
response. Finally, we develop a method for sample size estimation so that the model
may be used as a primary analysis method in clinical trials. We assess the impact of
correlation structure and drivers of response on the sample size required