Valid Methods for Meta-Analyses with Few Studies and Small Sample Sizes
Research on methods for meta-analysis has received increasing attention over the past decades. In the light of a large number of results that cannot be reproduced, the rigorous and robust aggregation of potentially heterogeneous quantitative findings on one research question is of great interest.
Different clinical trials with the same aim commonly provide varying results, even if the designs are very similar. Besides natural sampling variance, differences in study populations, clinical practice and the conduct of the studies can be found in practice. A valid interpretation of research results depends therefore on adequate modelling of such heterogeneity. Small sample sizes and insufficiently validated research results can be found in many areas of science. These circumstances highlight the necessity for meta-analysis methods that provide valid results even under difficult conditions including
small to moderate sample sizes
few studies only
In this project we develop flexible and valid statistical methods for different meta-analytic models that do not require strong model assumptions and facilitate a clear interpretation of the results including statistical inference. In order to make recommendations for the appropriate application of the new methods, these are thoroughly examined in extensive Monte Carlo simulation studies and will be applied to current data sets from a variety of fields including medicine. The methods developed are implemented in open-source software with comprehensive documentation ensuring widespread use.
This project will be a joint venture of the Department of Medical Statistics of the University Medical Center Göttingen and the Chair Mathematical Statistics and Applications in Industry at the Technical University of Dortmund and is funded by the DFG (German Research Foundation).
Researchers and Collaborators
Prof. Tim Friede Head of the Department of Medical Statistics University Medical Center Göttingen and investigator of the project.
Prof. Markus Pauly Head of the Chair Mathematical Statistics and Applications in Industry Technical University of Dortmund and investigator of the project.
Dr. Christian Röver Research Associate in the working group Statistical Methods for Clinical Trials at the Depatment of Medical Statistics.
M.Sc. Thilo Welz Research Associate and Doctoral Student at the Institute of Statistics, Technical University of Dortmund
Prof. Sofia Dias Professor of Health Technology Assessment at the University of York, part of the leadership team that delivers technology assessment reports for the National Institute for Health and Care Excellence (NICE) through York's NIHR funded TAR programme and lead author of the recent Wiley book on Network Meta-analysis for Decision Making
Prof. Christopher Schmid Professor of Biostatistics und Co-Director des Center for Evidence Synthesis in Health an der Brown School of Public Health, Providence, Rhode Island
Associate Professor Wolfgang Viechtbauer Associate professor of methodology and statistics in the Department of Psychiatry and Neuropsychology (Faculty of Health, Medicine, and Life Sciences) and the School for Mental Health and Neuroscience (Division 2: Mental Health) at Maastricht University