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Applied Statistics – University of Reading

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Applied Statistics

Applied statistics has a rich history at Reading: numerous methodological developments by researchers in this group have given rise to important impacts in a wide range of applications.

Our current research continues in this tradition, with the development of new statistical methods for analysis of large and complex data sets being increasingly important in the current era of 'big data'.



Current research interests are predominantly aligned to four overlapping areas.

Medical Statistics

appl_stats_research_medical_statsIn recent years there has been considerable interest in accelerating and refining the drug development process. Statistical innovations in trial design have led to the availability of new approaches to address these objectives. Our group at Reading is primarily involved in the development of methodology within the fields of adaptive designs, in particular adaptive seamless designs & group-sequential trials, pharmacogenetics and pharmacokinetics.

We have strong research links with other academic groups in this field including those based at the Universities of Warwick, Sheffield and Leeds. We also collaborate with both the pharmaceutical industry and public sector institutions. Within our group, several PhD students are studying for the qualification part-time whilst working in the Industry.

People: Sue Todd, Fazil Baksh

Statistical Genetics

appl_stats_research_geneticsRecent developments in whole genome sequencing have led to an explosion in the availability of genetic data. The main challenge is now in drawing inferences from these data. Our group develops methods for achieving this, and uses such methods in applied studies. Areas of particular interests are genome-wide association studies (in both humans and bacteria), family and population genetics for whole genome data, and inference for coalescent models and pathogenomics.

We collaborate with the Modernising Medical Microbiology consortium and East Malling Research, the Infectious Disease Research Centre at Massey University New Zealand, along with the Microbiology, Evolutionary Biology and Crop Research groups at Reading.

People: Fazil Baksh

Bayesian Computation

appl_stats_research_bayesianThe Bayesian approach to statistical inference has seen major successes in the past twenty years, finding application throughout science, engineering, finance and other disciplines. The main driver of these successes was the development of Monte Carlo computational methods to perform statistical inference. Our group at Reading is playing an active part in the development of next-generation Monte Carlo techniques, to exploit the increasing size and complexity of modern data sets.

Particular areas of expertise are approximate Bayesian computation, sequential Monte Carlo, and approximate and adaptive MCMC methods. Recent applications include genetics, neuroscience, signal processing and network analysis.

We have a regular Reading Group in Bayesian Computation, also involving members of the Data Assimilation, Polymer Physics, Ecology and Evolutionary Biology groups. This is a part of a university-wide network, the 'Bayes Group', of researchers interested in the use of Bayesian statistics.

Statistics of Extremes

A graphExtreme value theory provides a rigorous and prolific framework for analysing rare events with severe impact. The development of extremes over space and/or time rests on the celebrated max-stable processes. A recent offspring of extreme value theory is the generalised Pareto process.

Our group has recognised contributions in developing statistical tools for rare events with application to environmental data. We have also established strong international ties with other academics in the field.

People: Claudia Neves


Name Position Telephone
+44 (0) 118 378
Professor Sue Todd Professor of Medical Statistics 8917 s.c.todd
Dr Fazil Baksh Lecturer 8034 m.f.baksh
Dr Claudia Neves Lecturer 7931 c.neves

Emeritus Professor

Professor Robert N Curnow

Former Group Members

  • Dr Richard Everitt

Seminars and events

Our lively research environment is supported by a statistics seminar series and research workshops (with strong links to the Probability and Stochastic Analysis theme), together with a strong presence in the Royal Statistical Society local group.

Latest Publications

Jump to: 2020 | 2019
Number of items at this level: 6.


Dimairo, M., Pallmann, P., Wason, J., Todd, S., Jaki, T., Julious, S., Mander, A., Weir, C., Koenig, F., Walton, M., Nicholl, J., Coates, E., Biggs, K., Hamasaki, T., Proschan, M., Scott, J., Ando, Y., Hind, D. and Altman, D. (2020) The adaptive designs CONSORT Extension (ACE) statement: a checklist with explanation and elaboration guideline for reporting randomised trials that use an adaptive design. British Medical Journal, 369. m115. ISSN 1468-5833 doi:

Flight, L., Julious, S., Brennan, A., Todd, S. T. and Hind, D. (2020) How can health economics be used in the design and analysis of adaptive clinical trials? A qualitative analysis. Trials, 21. 252. ISSN 1745-6215 doi:

Kimani, P. K., Todd, S., Renfro, L. A., Glimm, E., Khan, J. N., Kairalla, J. A. and Stallard, N. (2020) Point and interval estimation in two-stage adaptive designs with time to event data and biomarker-driven subpopulation selection. Statistics in Medicine, 39 (19). pp. 2568-2586. ISSN 0277-6715 doi:

Stallard, N., Todd, S., Ryan, E. G. and Gates, S. (2020) Comparison of Bayesian and frequentist group-sequential clinical trial designs. BMC Medical Research Methodology, 20 (1). ISSN 1471-2288 doi:


Abery, J. E. and Todd, S. (2019) Comparing the MAMS framework with the combination method in multi-arm adaptive trials with binary outcomes. Statistical Methods in Medical Research, 28 (6). pp. 1716-1730. ISSN 0962-2802 doi:

Stallard, N., Todd, S., Parashar, D., Kimani, P. K. and Renfro, L. A. (2019) On the need to adjust for multiplicity in confirmatory clinical trials with master protocols. Annals of Oncology, 30 (4). pp. 506-509. ISSN 1569-8041 doi:

This list was generated on Tue Sep 22 19:11:20 2020 UTC.

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