Bayesian nonparametric hierarchical meta-regression: a flexible approach for modeling multiple biases when combining studies of varying quality and different types

• Applicant: Privatdozent Dr. Pablo-Emilio Verde
• Subject Area: Epidemiology and Medical Biometry/Statistics
• Term: since 2022
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 503988801

The objective of this project is to provide a flexible approach for jointly modeling effectiveness and biases in a meta-analysis that combines results from studies of different types (e.g., RCTs, observational studies) and varying quality. We investigate if Bayesian nonparametric methods of the Hierarchical Meta-Regression provide a more data-driven and robust approach against model misspecification in meta-analysis. The hierarchical meta-regression model explicitly distinguishes two sub-models: a sub-model used to handle the data collection process (e.g., modeling internal and external validity bias), and a sub-model used to answer the research questions (e.g., effectiveness, prognosis). The potential advantage of Bayesian nonparametric methos will be investigated in one or both of these sub-models. An R package will be developed based on these methods. The project aims to make a significant contribution to the assessment of health technologies.

• DFG Programme: Research Grants
• International Connection: United Kingdom, USA
• Co-Investigator: Professor Dr. Andreas Krieg
• Cooperation Partners: Professorin Dr. Sylwia Bujkiewicz; Professor Gary Rosner