Imperial College London


Faculty of MedicineSchool of Public Health

Clinical Trials Unit Statistician







Stadium HouseWhite City Campus





Matyas Szigeti is Clinical Trial Statistician at Imperial Clinical Trials Unit at Imperial College London since 2014. He has experience evaluating drug and surgical interventions across a broad range of therapeutic areas including asthma and pulmonary diseases, cardiovascular diseases, hepatitis and in vitro fertilisation. His statistical research interests are extreme value statistics, generalisability of clinical trial results and missing data imputation. He provided expert reviews for research proposals of the NIHR HTA Programme.

Current studies include: RECITAL, NUC-B, MICAH and C-19-ACS whilst past studies include Y14, EVRA, Rituxilup, HARMONY, Kisspeptin and AZALEA.

He obtained a BSc in Military and Safety Engineering in 2010 from Obuda University, Hungary and an MSc in Biomedical Engineering from Budapest University of Technology and Economics in 2012 and an  MSc in Medical Statistics from University of Leicester in 2014.



Tan P-T, Cro S, Van Vogt E, et al., 2021, A review of the use of controlled multiple imputation in randomised controlled trials with missing outcome data, Bmc Medical Research Methodology, Vol:21, ISSN:1471-2288

Gohel M, Mora J, Szigeti M, et al., 2020, Long-term clinical and cost-effectiveness of early endovenous ablation in venous ulceration (The EVRA randomized clinical trial), Jama: Journal of the American Medical Association, ISSN:0098-7484

Poulter NR, Savopoulos C, Anjum A, et al., 2018, Randomized crossover trial of the impact of morning or evening dosing of antihypertensive agents on 24-hour ambulatory blood pressure: the HARMONY trial, Hypertension, Vol:72, ISSN:0194-911X, Pages:870-873


Szigeti M, Ferenci T, Kovacs L, 2020, The use of block maxima method of extreme value statistics to characterise blood glucose curves, 2020 IEEE 15th International Conference of System of Systems Engineering (SoSE), IEEE

Szigeti M, Ferenci T, Kovacs L, 2020, The use of peak over threshold methods to characterise blood glucose curves, 2020 IEEE 14th International Symposium on Applied Computational Intelligence and Informatics (SACI), IEEE

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