Integrative Analysis of Randomized Trial and Real-World Data via Novel Meta-Analysis and Clustering

Music City Center 

We propose a novel two-stage model-based method for the integrative analysis of randomized trial (RT) and real-world (RW) data. In the first stage, a Bayesian nonparametric (BNP) model is applied for efficient data aggregation, providing clustering of combined data while ensuring similarity between RW and RT distributions within each cluster. To retain only comparable RW samples, those clustering exclusively without RT samples are filtered out. We construct the BNP model using the geometric weights prior, which naturally addresses the label-switching issue, a well-known limitation of Bayesian model-based clustering methods, requiring computationally intensive relabeling for correction.
The clustering outcomes from the BNP model are vital for the next stage, where we develop a new meta-analysis that accounts for heterogeneity across clusters. Our model adjusts for RW-RT similarity within clusters, ensuring that cluster-specific parameters with greater within-cluster similarity contribute more to estimating the grand parameters. Also, clusters are weighted proportional to their RT sample size to prevent larger, less reliable RW data from dominating the estimation.

Bayesian nonparametric model

Geometric weights prior

Meta-analysis

Randomized clinical trial

Real-world data 

Section on Bayesian Statistical Science