Individual patient data meta-analysis can utilize patient-level characteristics to investigate different treatment effects in potentially small subgroups. Yet, when the evidence base is small due to small subgroups and/or a small number of studies, treatment effect estimates are associated with large uncertainty. We will particularly focus on network meta-analysis, which accommodates the need to simultaneously synthesize evidence on multiple treatments. Specifically, we will develop a framework to explore subgroup treatment effects and treatment effects conditional on post-randomization characteristics. This will provide methods to 1) identify patient characteristics with strong predictive properties regarding the outcome of interest 2) estimate treatment effects within the selected subgroups, and 3) provide recommendations for adding data via new studies.
(supervised by PI Nikolakopoulou)
Perform original statistical research: development of statistical methods for prediction models, quantifying heterogeneity and develop methods for making concrete recommendations for future research.
(supervised by PI Schramm)
Developing a protocol for selecting patient characteristics in the context of persistent depression, dealing with directionality in bias in meta-analysis, and interpret evidence based on clinical judgements. Further tasks comprise administration of study documents and materials, literature search, and evaluation of study data.
Institute of Medical Biometry and Statistics,
Faculty of Medicine and Medical Center –
University of Freiburg
