<p>This series of papers explores the value of and mechanisms for using a heterogeneity test to compare treatment differences between the individual outcomes included in a composite outcome. Trialists often combine a group of outcomes together into a single composite outcome based on the belief that all will share a common treatment effect. The question addressed here is how this assumption of homogeneity of treatment effect can be assessed in the analysis of a trial that uses this type of composite outcome. A class of models that can be used to form such a test involve the analysis of multiple outcomes per person, and adjust for the association due to repeated outcomes being observed on the same individuals. We compare heterogeneity tests from multiple models for binary and time-to-event composite outcomes, to determine which have the greatest power to detect treatment differences for the individual outcomes within a composite outcome. Generally both marginal and random effects models are shown to be reasonable choices for such tests. We show that a treatment heterogeneity test may be used to help design a study with a composite outcome and how it can help in the interpretation of trial results.</p> / Doctor of Philosophy (PhD)