Individual Participant Data (IPD) meta-analysis (MA) is widely considered the gold standard method for synthesizing evidence from multiple studies. Unlike traditional aggregate data meta-analysis, IPD-MA offers several advantages, making it an invaluable tool for research and clinical decision-making.
In this web clinic, Georgios Seitidis and Sofia Tsokani will provide a brief overview of IPD meta-analysis and discuss the basic concepts.
Presenter Bios
Dr. Georgios Seitidis is a statistician with a strong interest in evidence synthesis and biostatistics. He completed his PhD in Biostatistics at the University of Ioannina, Greece, focusing on evaluating fundamental assumptions of (component) network meta-analysis through variable selection techniques. Currently, he is a postdoctoral researcher at the Department of Psychology, University of Ioannina. He has been involved in several methodological and applied projects in biostatistics, particularly in evidence synthesis and network meta-analysis. In addition to his expertise in aggregate data meta-analysis, he has been actively involved in several individual patient data (IPD) meta-analyses. His research interests include systematic reviews and statistical modeling for meta-analysis and network meta-analysis. In addition to his theoretical contributions, he actively develops statistical software packages in R.
Sofia Tsokani is a Statistics Editor in Cochrane’s Methods Support Unit and post-doctoral researcher at the School of Medicine of the Aristotle University, Greece. She is a biostatistician, with a PhD obtained from the University of Ioannina in Greece. Her PhD studies focused on evidence synthesis methods, with an emphasis on meta-analysis and network meta-analysis of diagnostic tests. She worked as a research associate in biostatistics prior to joining MSU; during this time, she was involved in several methodological and empirical projects, mainly in the field of network meta-analysis. Her research interests encompass systematic reviews, and statistical modelling for meta-analysis and network meta-analysis of both interventions and diagnostic tests.
