Statistical Science


Dr Menelaos Pavlou

PositionSenior Research Associate
Phone (external)+44(0)20 3108 3227
Phone (internal)53227
ThemesBiostatistics, General Theory and Methodology

* @ucl.ac.uk

Biographical Details

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.

Research Interests

Risk Prediction Modelling, Analysis of Clusted Data, Informative Cluster Size, Missing data, Penalised Regression, Methods for comparing Institutional Performance.

Selected publications

  • 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.