Theodoros Evrenoglou, PhD
As a Postdoctoral Research Associate within the CRESS METHODS team, my main interest is focused on the advancement of statistical methods for network meta-analysis (NMA). Specifically, my work involves the development of innovative NMA techniques for ranking multiple treatments and analyzing sparse data, among other areas of statistical research. In addition to my theoretical contributions, I am actively involved in various applied projects, with a particular focus on COVID-19 research. I have been a key member of the COVID-NMA project since 2020, where I have contributed my expertise as a statistician.
Beyond my research contributions, I am also deeply committed to developing software that can enable the broader application of statistical methods in medical research.
Statistical methods for network meta-analysis and pairwise meta-analysis
Handling sparse data
Ranking methods for multiple treatments
Evrenoglou, T, White, IR, Afach, S, Mavridis, D, Chaimani, A. Network meta-analysis of rare events using penalized likelihood regression. Statistics in Medicine. 2022. doi:10.1002/sim.9562 [open access paper]
Evrenoglou, T., Boutron, I., Seitidis, G., Ghosn, L. and Chaimani, A. (2023), metaCOVID: A web-application for living meta-analyses of COVID-19 trials. Res Syn Meth. doi: https://doi.org/10.1002/jrsm.1627 [open access paper]
Evrenoglou, T., Metelli, S., Thomas, J. S., Siafis, S., Turner, R. M., Leucht, S., & Chaimani, A. (2023). Sharing information across patient subgroups to draw conclusions from sparse treatment networks. [open access paper]