Statistical Science



Theme Overview

This theme has a research programme that encompasses the development, evaluation, and application of statistical and data science methods to address questions in health research. The overarching aim is to identify and apply the best possible statistical methods in health research, enabling the translation of reliable research findings to health care.

Much of the work within this theme is interdisciplinary and collaborative. There are established links with clinical and health research groups within UCL and beyond. In particular several staff members have joint posts with the UCLH/UCL Biomedical Research Centre (BRC) and the UCL Primary Care and Mental Health Clinical Trials Unit. There are strong collaborative links with the UCL Department of Applied Health Research, UCL Division of Psychiatry, LSE and Imperial College (Epidemiology and Biostatistics).

Current methodological research topics include risk prediction models, modelling clustered data, multistate models, model selection, survival models, trials methodology, methods for handling missing data, causal inference, regression discontinuity design, analysis of health economic data, multi-omics data integration, statistical / probabilistic genomics; mathematical biology; network models in genomics, and Bayesian Statistics.

This theme includes the Biostatistics Group and the Statistics  for Health Economic Evaluation Group. Researchers from the Statistics for Health Economic Evaluation Group are founding and key members of two international consortia (ConVOI) and R-HTA, working on the development and dissemination of methods for the analysis of the value of information and for the establishment of statistical methods and software in health technology assessment.

Theme members

Theme Members 

Rumana Omar (Theme lead)

Mariam Adeleke 
Gareth Ambler 
Javier Rubio Alvarez 
Gianluca Baio 
Julie Barber 
Tom Bartlett 
Nathan Green 
Takoua Jendoubi 
Baptiste Leurent 
Menelaos Pavlou 
Giampiero Marra 
Chen Qu 
Ardo Van Den Hout 

Selected recently funded research projects:

  • Cancer Intervention and Surveillance Modelling Network (CISNET) Modeling Precision Interventions for Prostate Cancer Control. NIH (£970,000)
  • MRC skills development Fellowship awarded to fund three years’ independent work developing novel statistical methodology for biomedical science applications. (£340 304)
  • Mixed Semi-Continuous Copula Additive Regression Models with Applications in Insurance and Health Care. EPSRC (440,000)


Biostatistics Group research grants

Statistics for Health Economic Evaluation Group research grants