Biostatistics; Epidemiology; Models, Statistical
My main research focus is the design and analysis of clinical trials in medical statistics. The focus in statistical methodology is on the theory of multiple hypotheses testing controlling the FWER or FDR and on adaptive and group-sequential designs in the context of a clinical trial or in high-dimensional data.
Techniques, methods & infrastructure
An R-package for testing the global null hypothesis with the omnibus test is available (https://github.com/ThomasTaus/omnibus/blob/master/DESCRIPTION). An extended R-package including the test for subgroups is in preparation.
- Repeated Significance Tests controlling the False Discovery Rate (2009)
Source of Funding: FWF (Austrian Science Fund), Hertha Firnberg grant
- Zehetmayer, S., Posch, M. and Koenig, F. (2022) ‘Online control of the False Discovery Rate in group-sequential platform trials’, Statistical Methods in Medical Research, 31(12), pp. 2470–2485. Available at: http://dx.doi.org/10.1177/09622802221129051.
- Futschik, A., Taus, T. and Zehetmayer, S. (2018) ‘An omnibus test for the global null hypothesis’, Statistical Methods in Medical Research, 28(8), pp. 2292–2304. Available at: http://dx.doi.org/10.1177/0962280218768326.
- Zehetmayer, S. and Posch, M. (2012) ‘False discovery rate control in two-stage designs’, BMC Bioinformatics, 13(1). Available at: http://dx.doi.org/10.1186/1471-2105-13-81.
- Zehetmayer, S. and Posch, M. (2010) ‘Post hoc power estimation in large-scale multiple testing problems’, Bioinformatics, 26(8), pp. 1050–1056. Available at: http://dx.doi.org/10.1093/bioinformatics/btq085.
- Zehetmayer, S., Bauer, P. and Posch, M. (2008) ‘Optimized multi-stage designs controlling the false discovery or the family-wise error rate’, Statistics in Medicine, 27(21), pp. 4145–4160. Available at: http://dx.doi.org/10.1002/sim.3300.