Finite mixture models to look at the causal effects of process variables
State of the art talk by Richard Emsley, University of Manchester, UK
Chair: Tim Croudace, Wednesday 22nd July, 11.45 - 12.30, Palmeston Lecture Theatre, Fisher Building.
Using psychological treatment trials as an example of complex
interventions, we review statistical methods which evaluate both direct and
indirect effects, in the presence of hidden confounding between the process
variables and outcome. We review the historical literature on mediation and
moderation of treatment effects before introducing two methods from within
the causal inference literature, principal stratification (finite mixture
models) and structural mean models. We demonstrate how these can be applied
in the context of mediation before discussing approaches and assumptions
necessary for attaining identifiability of key parameters of the basic
causal model. We describe how moderation can occur through
post-randomisation variables, and extend the principal stratification
approach to multiple group methods with explanatory models nested within
the principal strata. We illustrate the new methodology with motivating
examples of randomised trials from the mental health literature.