Description: Using data from various clinical trials and other studies, methodology and statistical concepts are presented that remain solid also when studies are very small, such as in rare diseases, or very large. In the former case, we need to ensure that the statistical methodology leads to reliable estimates, without being riddled with computational instability. In the latter case, we need to ensure that running times remain feasible. Specific attention is devoted to the potential of surrogate markers and the omnipresent problem of incomplete data. The focus is on concepts and illustration, not on mathematical detail.
Lecturer: Prof. Geert Molenberghs – Hasselt University and KU Leuven, Belgium
Geert Molenberghs is Professor of Biostatistics at UHasselt and KU Leuven. He received a degree in mathematics (1988) and a Ph.D. in biostatistics (1993) from UAntwerpen. He published on surrogate markers in clinical trials, and categorical, longitudinal, and missing data. He was Editor for Applied Statistics, Biometrics, and Biostatistics, and is currently Executive Editor of Biometrics. He was President of the International Biometric Society. He is Fellow of the American Statistical Association, received the Guy Medal in Bronze from the Royal Statistical Society, and held visiting positions at Harvard. He is founding director of the Center for Statistics at UHasselt and of the Interuniversity Institute for Biostatistics and statistical Bioinformatics (UHasselt and KU Leuven). He received research funding from FWO, IWT, the EU (FP7), U.S. NIH, U.S. NSF, UHasselt, KU Leuven, ECDC, and EMA. He is member of the Belgian Royal Academy of Medicine. He has been active (as advisor, researcher, and communicator) in the SARS-CoV-2 pandemic response. He has taken part in various grant funded research programs on rare diseases, including IDEAL, EJP RD, ERDERA, and RealiseD.
