Hierarchical meta-regression: a unified approach to model multiplicity of bias in combining randomized and non-randomized evidence in meta-analysis

• Applicant: Privatdozent Dr. Pablo-Emilio Verde
• Subject Area: Epidemiology and Medical Biometry/Statistics
• Term: from 2015 to 2017
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 269346715

The aim of this project is to provide an integrated approach for bias modeling when randomized and non-randomized evidence is combined in meta-analysis. The statistical approach to be developed is a hierarchical meta-regression model. This model allows combining pieces of evidence of different study-types (e.g. RCTs, observational cohort studies, etc.) and different data types (aggregated results from publications, individual patient data, etc.). The model explicitly distinguishes parameters used for data collection processes (modeling of bias) and parameters used for clinical questions (e.g. treatment effects in group of patients or diagnostic accuracy). Based on this approach an R-package will be implemented. The project should make a significant contribution to statistical evidence synthesis and health technology assessment.

• DFG Programme: Research Grants