Identifying High-Risk Subgroups in Meta-Analyses with Hierarchical Bayesian Sparse Modeling

2672 

Contributed Abstract 

Poster 

Grace Nie (1), Christine Peterson (2), Shouhao Zhou (3)

(1) N/A, N/A, (2) University of Texas MD Anderson Cancer Center, N/A, (3) Pennsylvania State University, N/A

Christine Peterson  
University of Texas MD Anderson Cancer Center

Shouhao Zhou  
Pennsylvania State University

Grace Nie  
N/A

Grace Nie  
N/A

With immunotherapy drug development, meta-analyses have been used to assess adverse events in large sample sizes. However, toxicity profiles vary across adverse event categories by disease type and treatment. There is increasing interest in identifying high-risk groups for closer toxicity monitoring, but this effort is hindered by sparsely observed outcomes and a high number of potential risk factors for different types of adverse events. Traditional meta-analysis methods, such as fixed and random effects models, fail to address this issue and often yield biased estimates for rare events. We frame the problem as a Bayesian variable selection approach to identify high-risk groups and use the horseshoe prior to address sparsity in linking adverse event probabilities to study-level covariates. While earlier Bayesian horseshoe prior models exist, they have limitations and may not fully utilize available information. Building on the horseshoe prior model, we propose a Bayesian feature selection model that selects both main and interaction effects, using main effects selection to help form a hierarchical structure that facilitates interaction selection.

Bayesian feature selection|Meta-analyses|Horseshoe prior|Categorical data analysis| |

Section on Bayesian Statistical Science

Applications in Life Sciences and Medicine