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

Area of Science:

• Biostatistics
• Clinical Trial Analysis
• Personalized Medicine

Background:

• Model-based recursive partitioning (MOB) and metaMOB are statistical tools for identifying patient subgroups with differential treatment effects.
• Leveraging individual participant data (IPD) from multiple trials is essential for detecting small treatment benefits and identifying responders, especially in costly, large-scale studies.
• Heterogeneity in treatment effects across individuals necessitates advanced analytical approaches.

Purpose of the Study:

• To apply MOB and metaMOB for identifying subgroups with differential treatment effects in the context of non-specific low back pain.
• To explore the impact of heterogeneity in intercepts and treatment effects within IPD meta-analyses.
• To demonstrate the utility of these methods for personalized treatment strategies.

Main Methods:

• Utilized model-based recursive partitioning (MOB) and its extension, metaMOB.
• Employed random effects to model treatment effect heterogeneity in meta-analyses.
• Applied methods to synthetic data derived from a subset of the Patel et al. IPD meta-analysis on low back pain.
• Investigated heterogeneity in both intercepts and treatment effects.

Main Results:

• The study successfully applied MOB and metaMOB to identify potential subgroups with differential treatment effects.
• Analysis highlighted the importance of considering heterogeneity in both baseline characteristics (intercepts) and treatment responses.
• Findings suggest that these methods can uncover nuanced treatment effects within diverse patient populations.

Conclusions:

• MOB and metaMOB are valuable tools for dissecting heterogeneity in IPD meta-analyses.
• Exploring heterogeneity in intercepts and treatment effects is critical for identifying patient subgroups who benefit most from interventions.
• These approaches support the development of personalized treatment strategies, particularly for conditions like low back pain.