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All Times EDT

Friday, September 25
Fri, Sep 25, 11:45 AM - 12:45 PM
Virtual
Poster Session

PS17-A Comparison of Bayesian Meta-Analysis Methods for Rare Adverse Events (301118)

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Chenguang Wang, The Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins University 
Hwanhee Hong, Department of Biostatistics and Bioinformatics, Duke University 
Gary Rosner, The Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins University 
*Jinyi Zhou, Department of Biostatistics and Bioinformatics, Duke University 

Keywords: meta-analysis, Bayesian analysis, rare events, regulatory science, rosiglitazone

Meta-analysis is a commonly used statistical technique for combining results from multiple studies. A common issue that we encounter when applying a meta-analysis for extremely rare events is sparsity, leading to zero-event trials that may cause extremely skewed distributions of event frequencies and insufficient statistical power to estimate the effect heterogeneity across studies. Bayesian meta-analysis models are often used to handle such issues due to their flexibility. In addition, we can easily study different model assumptions by employing a wide range of prior specifications. In this work, we compare various Bayesian meta-analysis models under a number of different prior distributions. We consider three meta-analysis models: (1) logistic regression model, (2) arm-based model, and (3) beta hyperprior model, each under two assumptions, common treatment effect (CTE) and heterogeneous treatment effect (HTE). For all models, we consider a wide range of priors (from weakly to strongly informative) for model parameters. For the HTE logistic model, we consider uniform, half-Cauchy, Pareto, and half-normal priors for the between study heterogeneity. For the arm-based model, we consider different Wishart priors for the covariance matrix of random effects. For the beta hyperprior model, we consider noninformative priors, including Jeffreys’ prior and a robust mixture prior for event probabilities. We compare performance of the different models via simulation studies under different degrees of infrequency (or rareness), and then illustrate them using a real meta-analysis data: trials for rosiglitazone on the risks of myocardial infarction and of death from cardiovascular causes.