|Position||Senior Research Associate|
|Phone (external)||+44(0)20 3108 3227|
|Themes||Biostatistics, General Theory and Methodology|
Menelaos has been working as a Senior Research Associate in Biostatistics at the Department of Statistical Science, UCL since January 2018. He is part-based at the National Institute of Cardiovascular Outcomes Research (NICOR) at Barts Health NHS Trust where he provides statistical oversight of the analytical team. Menelaos completed a degree of Mathematics in 2004 at the Aristotel's University of Thessaloniki, Greece, followed by an MSc in Statistics (2005) in the Department of Statistical Science, UCL.
After having worked for a consulting company, he pursuit a part-time PhD in Medical Statistics in the department of Statistical Science, UCL (2007), while working as a Medical Statistician at the Centre of Sexual Health and HIV Research, UCL. He was awarded with a PhD in 2012, and until 2016 was employed as a post-doctoral research associate in the Department of Statistical Science, UCL.
Risk Prediction Modelling, Analysis of Clusted Data, Informative Cluster Size, Missing data, Penalised Regression, Methods for comparing Institutional Performance.
- Pavlou M, Ambler G, Seaman RS, Guttman O, Elliott P, Omar RZ (2015). How to develop a more accurate risk prediction model when there are few events, BMJ, 351:h3868.
- Pavlou M, Ambler G, Seaman RS, De Iorio M, Omar R (2015). Review and evaluation of penalised regression methods for risk prediction in datasets with few events, Statistics in Medicine, doi: 10.1002/sim.6782.
- Pavlou M, Ambler G, Seaman RS, Omar RZ (2015). A note on obtaining correct marginal predictions from a random intercepts model for binary outcomes, BMC, Medical Research Methodology, 15:59, doi: 10.1186/s12874-015-0046-6.
- O'Mahony C, Jichi F, Pavlou M, Monserrat L et al. (2013). A novel clinical risk prediction model for sudden cardiac death in hypertrophic cardiomyopathy, European Heart Journal, doi:10.1093/eurheartj /eht439.
- Seaman S, Pavlou M, Copas A (2014). A Review of Methods for Handling Confounding by Cluster and Informative Cluster Size, Statistics in Medicine, doi:10.1002/sim.6277.
- Seaman S, Pavlou M, Copas A (2014). Methods for Observed-Cluster Inference when Cluster Size is Informative: a Review and Clari cations, Biometrics, doi: 10.1111/biom.12151.
- Bailey J, Pavlou M, Copas A, McCarthy O, Karswell K (2013). The Sexunzipped pilot trial: optimizing the design of online randomized controlled trials, Journal of Medical Internet Research,15(12):e278.
- Pavlou M, Seaman S, Copas A (2013). An examination of a method for marginal inference when the cluster size is informative, Statistica Sinica, 23, pp. 791-808.