# Subgroup identification using individual participant data from multiple trials: An application in low back pain

## TL;DR

This paper explores using statistical methods to find subgroups of patients who benefit most from low back pain treatments by analyzing data from multiple trials.

## Contribution

The paper applies and evaluates MOB and metaMOB for subgroup identification in IPD meta-analyses of low back pain treatments.

## Key findings

- MOB and metaMOB can identify subgroups with differential treatment effects in low back pain.
- Pooling individual-participant data from multiple trials helps detect treatment heterogeneity.
- The study highlights the importance of modeling heterogeneity in treatment effects for effective subgroup identification.

## Abstract

Model-based recursive partitioning (MOB) and its extension, metaMOB, are tools for identifying subgroups with differential treatment effects. When pooling data from various trials the metaMOB approach uses random effects to model the heterogeneity of treatment effects. 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 synthetic data based on a subset of the individual participant data meta-analysis by Patel et al.
1 Our study underscores the need to explore heterogeneity in intercepts and treatment effects to identify subgroups with differential treatment effects in IPD meta-analyses.