Vortrag von John Ioannidis (Stanford) am 4. November 2019
Reproducible research: transparency and translational impact
John P.A. Ioannidis, MD, DSc
C.F. Rehnborg Chair in Disease Prevention; Professor of Medicine, of Health Research and Policy, of Biomedical Data Science, and of Statistics, Stanford University; Co-director, Meta-Research Innovation Center at Stanford (METRICS)
Abstract: Theoretical insights, simulations, and empirical data converge that in the current research environment a substantial segment of published research yields results that are not credible. Moreover, among the results that are credible a large share cannot be translated meaningfully and thus end up being not useful. The lecture will assess the scope of this evidence and will focus on solutions that have been proposed to make research more reproducible and with greater translational impact. Many of these solutions center around optimizing transparency. Transparency can clearly help reproducibility, but this alone may not suffice to make research outcomes also more useful. Different solutions will be discussed with emphasis on biomedical and social sciences, although the challenges cover all scientific fields and comparative data will be presented across different fields.