  Model-based recursive partitioning (MOB) and its extension, metaMOB, are
potent tools for identifying subgroups with differential treatment effects. In
the metaMOB approach random effects are used to model heterogeneity of the
treatment effects when pooling data from various trials. In situations where
interventions offer only small overall benefits and require extensive, costly
trials with a large participant enrollment, leveraging individual-participant
data (IPD) from multiple trials can help identify individuals who are most
likely to benefit from the intervention. We explore the application of MOB and
metaMOB in the context of non specific low back pain treatment, using
synthesized data based on a subset of the individual participant data
meta-analysis by Patel et al. Our study underscores the need to explore
heterogeneity in intercepts and treatment effects to identify subgroups with
differential treatment effects in IPD meta-analyses.
