Mediation analysis allows decomposing a total effect into a direct effect of the exposure on the outcome and an indirect effect operating through a number of possible hypothesized pathways. Recently, we provided a novel decomposition of the total effect that unifies mediation and interaction when multiple mediators are present. We illustrated the properties of the proposed framework for multiple mediators and interactions, in a secondary analysis of a pragmatic trial for the treatment of schizophrenia (SZ). Analyses conducted in individual trials are not sufficiently powered to yield strong conclusions. We develop novel statistical methods to (i) address the issue of missing data, (ii) capture the complex underlying mechanisms of change, and (iii) integrate information from several efficacy trials to produce more powerful causal mediation and interaction analyses. We consider hierarchical linear modeling and multivariate meta-analysis approaches to estimate the causal contrasts that arise from the novel decomposition. We apply the approaches to quantify the role of symptoms and adverse events in explaining the effect of antipsychotics on social functioning in SZ patients.