[Show abstract][Hide abstract] ABSTRACT: -gllamm- provides a framework within which many of the more difficult analyses required for trials and intervention studies may be undertaken. Treatment effect estimation in the presence of non-compliance can be undertaken using instrumental variable (IV) methods. We illustrate how -gllamm- can be used for IV estimation for the full range of types of treatment and outcome measures and describe how missing data may be tackled on an assumption of latent ignorability. Alternative approaches to account for clustering and analyse cluster-randomised studies will also be described. Quality of life and economic evaluation of outcomes often makes use of discrete choice and stated preference experiments in which illness scenarios are assessed. We illustrate how -gllamm- can be used for the analysis of data from such studies, whether these are in the common form of paired comparisons or the more complex case where multiple scenarios are ranked. Examples from studies of a school-based smoking intervention, a re-employment encouragement experiment, a group therapy trial and of quality-of-life with rheumatoid arthritis will be considered.