Alexandros Rekkas, MSc

PhD Student

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Alexandros Rekkas is a PhD candidate at the Health Data Sciences Department of Erasmus University Medical Center. Before starting his PhD, he obtained his BSc (2015) in Mathematics from Aristotle University of Thessaloniki, Greece and his MSc (2017) in Statistics from Catholic University of Leuven, Belgium.

His current research interests focus on predictive approaches for the assessment of treatment effect heterogeneity. Along these lines, he is actively developing the RiskStratifiedEstimation R-package that implements a standardized framework for the evaluation of treatment effect heterogeneity, leveraging information from a global network of healthcare databases.



Rekkas, Alexandros, et al. "Individualized treatment effect was predicted best by modeling baseline risk in interaction with treatment assignment." arXiv preprint arXiv:2205.01717 (2022).
van Klaveren DRekkas AAlsma J, et al. COVID outcome prediction in the emergency department (COPE): using retrospective Dutch hospital data to develop simple and valid models for predicting mortality and need for intensive care unit admission in patients who present at the emergency department with suspected COVID-19. 
Rekkas, A., Paulus, J.K., Raman, G. et al. Predictive approaches to heterogeneous treatment effects: a scoping review. BMC Med Res Methodol 20, 264 (2020).

Rekkas, Alexandros, et al. "A standardized framework for risk-based assessment of treatment effect heterogeneity in observational healthcare databases." arXiv preprint arXiv:2010.06430 (2020).

Daniëlle Verver, Alexandros Rekkas, Claus Garbe, David van Klaveren, ..., Dirk J. Grünhagen, The EORTC-DeCOG nomogram adequately predicts outcomes of patients with sentinel node–positive melanoma without the need for completion lymph node dissection, European Journal of Cancer, Volume 134, 2020, Pages 9-18, ISSN 0959-8049,

Bollaerts K, Rekkas A, De Smedt T, Dodd C, Andrews N, Gini R (2020). Disease misclassification in electronic healthcare database studies: Deriving validity indices—A contribution from the ADVANCE project. PLoS ONE 15(4): e0231333.