Centre for Biostatistics

Statistical expertise in biological, medical and health research at The University of Manchester.



We have an international reputation for innovative statistical research, excellent teaching and interdisciplinary collaboration over more than 20 years.

Starting as the Biostatistics Group in 1997, the Centre is now a professional focus for medical statisticians and Biostatisticians within the Faculty of Biology, Medicine and Health at The University of Manchester.

We undertake a blend of high quality applied and methodological research which is complemented by relevant teaching activities. 

Our programme of research is collaborative and often international. It acts as the prime source of statistical expertise in the Faculty, providing support for the Manchester Clinical Trials Unit and the local office of the NIHR Research Design Service.



Our research

Underpinning all our work in collaborative clinical and healthcare research is a vibrant programme of funded methodological research.


Our staff have a wide range of methodological interests and strengths including:

Clinical trials and methodology

  • Improving the quality and efficiency of clinical trials
  • Novel clinical trial designs
  • Developing core outcomes set

Evidence synthesis

  • Improving the quality of systematic reviews and meta-analyses

Epidemiology and analysis of observational studies

  • Causal inference
  • Multi-omics
  • Use of primary care databases in research

Statistical modelling

  • Development of clinical prediction models
  • Use of Bayesian methods
  • Analysis of clustered or correlated data arising from measurements repeated over time.
Research areas

We collaborate closely with colleagues across the University and beyond on a wide-range of clinical projects, particularly in the areas of:

  • public health
  • stroke research
  • mental health
  • diabetes and diabetes prevention
  • subfertility
  • musculoskeletal diseases
  • behaviour change
  • hearing health
  • nursing
Social responsibility

It is our social responsibility, as collaborators and advisors in applied research programmes, to ensure best practice in project design, statistical analysis and the interpretation and presentation of the results.

We ensure, as far as possible, that our clinical and healthcare research collaborators are drawing valid and robust inferences and are avoiding making claims that are not justified by evidence.

This is supplemented by our commitment to the Research Design Service and a series of training workshops and seminars to update biostatisticians and others on developments in the field.


View all publications from the Centre in the University’s Research Explorer.

Our members

Staff in the Centre come from a variety of backgrounds, covering a number of areas of expertise across biology, medicine, health and statistics.

Members (A to Z)
  • Amin Vahdati, NIHR Predoctoral Fellow
  • Andy Vail, Professor of Clinical Biostatistics
  • Antonia Marsden, Research Fellow
  • Ashma Krishan, Research Fellow
  • Azita Rajai, Research Assistant (Hon)
  • Barbara Tomenson, Research Associate (Hon)
  • Blessing Nyakutsikwa, Research Associate
  • Bohan Zhang, PhD student
  • Calvin Heal, Research Fellow
  • Carlo Berzuini, Emeritus Professor of Biostatistics
  • Cath Fullwood, Research Fellow (Hon)
  • Chris Roberts, Emeritus Professor of Biostatistics
  • Chris Sutton, Senior Lecturer in Clinical Trial Statistics
  • Clare Hodgson, Christie
  • David Reeves, Emeritus Professor in Health Services Research
  • Emma Barrett, Research Assistant (Hon)
  • Fiona Holland, Research Associate
  • Fiskani Kondowe, PhD student
  • Hui Guo, Centre Lead and Reader in Biostatistics
  • Isla Gemmell, Senior Lecturer (Affiliated)
  • Jack Kelly, Honorary Research Associate
  • Jack Wilkinson, Senior Lecturer in Biostatistics
  • Jamie Kirkham, Head of Division and Professor of Biostatistics
  • Jamie Sergeant, Senior Lecturer
  • Katie Stocking, NIHR Doctoral Research Fellow
  • Lesley-Anne Carter, Lecturer in Biostatistics
  • Matthew Gittins, Senior Lecturer in Biostatistics
  • Mark Hann, Senior Lecturer
  • Melody Adesina, NIHR Predoctoral Fellow
  • Roberto Carrasco, Research Associate
  • Roseanne McNamee, Emeritus Professor of Epidemiological Statistics
  • Selman Mirza, Research Associate
  • Sarah Cotterill, Reader in Biostatistics
  • Sarah Rhodes, Senior Lecturer in Biostatistics
  • Steve Roberts, Senior Lecturer in Medical Statistics
  • Sebastian Bate, Research Assistant (Hon)
  • Tracey Farragher, Senior Lecturer in Healthcare Sciences
  • Wenhua Wei, Honorary Senior Research Fellow
  • Yvonne Sylvestre, Research Fellow
  • Zelpha D’Souza, PhD student
  • Zewen Lu, PhD student

Collaborate with us

The Biostatistics Collaboration Unit (BCU) in the Centre exists to provide statistical expertise for grant-funded research across the full spectrum of biological, medical and health research.

Investigators can apply for part-time and temporary statistical input on grants to fit their needs.

We can help with:

  • statistical work to be undertaken by professional statisticians;
  • reassurance for funders that data will be handled correctly;
  • higher quality publications more likely to be rated 3* or 4*;
  • full recovery of costs;
  • appointment to part-time, temporary posts;
  • attraction and retention of higher quality staff;
  • career progression and professional support for staff;
  • cover for prolonged absences.
How to access our services

To find out how we can help you, contact us at biostatsenquiries@manchester.ac.uk.

If we can support your project, the process is usually as follows:


  1. Grant applicants agree with a senior statistician, usually a co-applicant, the requirement for statistical input on their project.
  2. When the grant is awarded, the BCU identifies or appoints a research assistant, associate or fellow to be available and committed to the project at the required times.
  3. If investigators have grants requiring full-time statistical input, the appointee can join the BCU administratively but co-locate with the research team. This ensures research team membership for the statistician while avoiding professional isolation.

Teaching and resources

We contribute to a number of undergraduate and postgraduate courses, and provide training in statistics to both staff and students.


We support the Evidence Based Medicine (EBM) programme of the medical undergraduate degree in the Manchester Medical School.

We also teach biostatistics modules within the undergraduate programme in the Department of Mathematics.


We contribute to the development and implementation of the statistical components of the following courses:


Epidemiology Summer School

Alongside colleagues from the Division of Population Health, Health Services Research and Primary Care, we run an Epidemiology International Summer School, a three-week course covering the fundamentals of epidemiology, biostatistics and health economics.

Training and statistical advice

Training and advice for potential grant applications (including help in building research teams by finding possible collaborating methodologists) is provided through the Manchester Hub of the region’s NIHR Research Design Service (RDS) led from within the Centre.

Where projects require statistical input, the Centre is open to research collaborations, such as PhD supervisions and grant applications. The Centre may be able to supply statistical services where these can be appropriately funded. 

To arrange an appointment with a statistician, email biostatsenquiries@manchester.ac.uk.

Statistical resources

A collection of resources we use in our teaching and training to assist with statistical analysis.

SPSS handouts

This handout is an introduction to SPSS (Statistical Package for the Social Sciences). It covers:

  • data entry in SPSS
  • descriptive statistics
  • simple graphics
  • basic inference including the chi-squared test and the t-test
  • simple linear regression.

The methods are introduced through the analysis of data from an epidemiological study of dust exposure and respiratory disease.

The handout gives some guidance regarding the interpretation of results and presentation in a paper and project report.

SPSS notes (PDF, 6 MB)
Last updated: 15 March 2017 by Matthew Gittins

The following datasets are used with these notes:

Data file: foundry.sav (right-click link and choose ‘Save link as’ to download)
Last updated: 4 January 2006
File size: 14 KB

Data file: foundry.xls
Last updated: 4 January 2006
File size: 44 KB

Data file: foundrysyn.SPS
Last updated: 28 March 2006

StatsDirect handouts

This handout is an introduction to StatsDirect. It covers:

  • data entry in StatsDirect
  • descriptive statistics
  • simple graphics
  • basic inference including the chi-squared test and the t-test
  • simple linear regression.

The methods are introduced through the analysis of data from an epidemiological study of dust exposure and respiratory disease.

The handout gives some guidance regarding the interpretation of results and presentation in a paper and project report.

StatsDirect notes (PDF) prepared by Tripti Halder, Chris Roberts and Matthew Gittins of the Biostatistics Group, May 2017.
Last updated: 6 May 2017
File size: 3,934 KB

The following dataset is used with these notes:

Data file: foundry.xls
Last updated: 4 January 2006
File size: 44 KB

STATA handouts

STATA notes (PDF)
File size: 1,936 KB

‘How to’ STATA tutorials from the London School of Economics

Statistical support

Where projects require statistical support, the Centre is open to collaborations (student supervisions, grant applications), and may be able to supply statistical services where these can be appropriately funded. 

In the first instance, we suggest making contact by email: biostatsenquiries@manchester.ac.uk.

Please include:

  • Your name
  • Short paragraph explaining project
  • Questions (two to three)
  • A written draft of aims, objectives and outline design is helpful.


Book list

The following introductory texts are recommended:

  • Campbell, M.J. & Machin, D. (1990) Medical Statistics: A Commonsense Approach. West Sussex. John Wiley & Sons Ltd.
  • Altman, D.G. (1991) Practical Statistics for Medical Research. London: Chapman & Hall
  • Armitage, P. & Berry, G. (1994) Statistical Methods in Medical Research (3rd Edition). Oxford: Blackwells
  • Bland, M. (2000) An Introduction to Medical Statistics (3rd Edition). Oxford: Oxford University Press
  • Chalmers I. & Altman, D.G. (1998) Systematic Reviews London : BMJ Publishing
  • Everitt, B. (1994) Statistical Methods in Medical Investigations. 2nd ed.. London : Edward Arnold
  • Gardner, M.J. & Altman, D.G. (2000) Statistics with Confidence: Confidence Intervals and Statistical Guidelines.2nd Ed. London : BMJ Publishing
  • Pocock, S.J. (1988) Clinical Trials: A Practical Approach Chichester. John Wiley & Sons
  • Riley, R.D., Van der Windt, D.A., Croft, P., Moons, K.G.M. (eds) (2019) Prognosis Research in Healthcare: Concepts, Methods and Impact: Oxford University Press.


Start or continue your career in biostatistics at the Centre. Vacancies, fellowship and studentship opportunities will be posted here when they are available.


We welcome anyone wishing to apply for a National Institute for Health and Care Research or MRC fellowships who want to be based in the Centre.

Potential applicants should contact individual academics or Hui Guo (Centre Lead) in the first instance.

PhD studentships

Self-funded students interested in undertaking a PhD should contact individual academics to enquire about research opportunities.


Discover software modules developed by our members that can be used in Stata.


clsampsi is an ADO file which calculates the power for trials and the number of clusters and cluster sizes required for the difference of means or proportions in the presence of differential clustering effects between study arms.

A ‘rough’ approximation to the optimum allocation ratio for such a trial, given a desired power, is also available (under the optimal option).

Sample size and power calculation for trials with clustering is commonly based on a summary t-test. Where there is differential pattern of clustering between arms, heteroscedasticity is introduced into the summary measures comparison.

A summary measures statistical analysis should therefore be based on Satterthwaite’s approximate F test using modified degrees of freedom rather than a t-test.

Authors: Eva Batistatou and Chris Roberts

Further information: the command can be installed within Stata by typing ‘findit clsampsi’ at the command prompt.

dr (double robust)

dr computes a double-robust effect estimate for the effect of a treatment on an outcome given a set of confounders.

The confounding effect is adjusted for in two ways: by modeling the effect of both treatment and confounders on the outcome (outcome model), and by weighting observations according to the inverse of their probability of receiving the treatment they actually received (propensity model).

If either the outcome model or the propensity model is correct, the doubly robust estimate is unbiased. 

Author: Richard Emsley and Mark Lunt

Reference: Emsley R, Lunt M, Pickles A and Dunn G. The Stata Journal, 2008, Vol 8(3), pp.334-353.

Further information: the command can be installed within Stata by typing ‘search dr’ at the command prompt, or from the following link: Download the dr command

gllamm (generalised linear latent and mixed models)

These models include multilevel generalised linear regression models (extensions of the simple random intercept models that may be fitted in Stata using xtreg, xtlogit, xtpois to include multilevel and random coefficient models), multilevel factor models and multilevel structural equation models.

The latent variables (or random effects) can be assumed to have a multivariate normal distribution or to be discrete allowing nonparametric maximum likelihood estimation.

The common links and families of generalised linear models are available and responses can be of mixed type including continuous, censored, discrete, dichotomous, ordered categorical and unordered categorical.

Author: Andrew Pickles

Further information: Visit the GLLAMM website. The command can be installed within Stata by typing ‘ssc describe gllamm’ at the command prompt.

metaan (module for performing fixed or random effects meta analyses)

The metaan command performs a meta-analysis on a set of studies and calculates the overall effect and a confidence interval for the effect. The command also displays various heterogeneity measures: Cochrane’s Q, I-squared, H-squared and the between-study variance estimate.

Cochrane’s Q is the same across all methods, but the between-study variance estimate (and hence I-squared and H-squared) can vary between the dl and ml methods.  Only one method option must be selected.

Authors: Evan Kontopantelis and David Reeves

Reference: Kontopantelis E, Reeves D. (2010). The Stata Journal, Vol. 10, Issue 3, pp395-407.

Further information: within Stata type ‘ssc describe metaan’


metaeasy implements meta-analysis methodology in an Microsoft (Excel) add-in which is freely available and incorporates more meta-analysis models (including the iterative maximum likelihood and profile likelihood) than are usually available, while paying particular attention to the user-friendliness of the package.

Authors: Evan Kontopantelis and David Reeves

Reference: Kontopantelis E, Reeves D. (2009). Journal of Statistics Software, Vol. 30, Issue 7.

Further information: Visit the Statanalysis website 

metaeff (meta analysis module for effect sizes calculations)

The metaeff command provides a way to calculate the effect sizes (and the respective standard errors) of research studies, for use with meta-analysis methods.

The methods used for the calculations have been derived from the Cochrane Collaboration Handbook.

Authors: Evan Kontopantelis and David Reeves

Further information: within Stata type ‘ssc describe metaeff’

paramed (causal mediation analysis using parametric regression models)

paramed performs causal mediation analysis using parametric regression models.

Two models are estimated: a model for the mediator conditional on treatment (exposure) and covariates (if specified), and a model for the outcome conditional on treatment (exposure), the mediator and covariates (if specified).

It extends statistical mediation analysis (widely known as Baron and Kenny procedure) to allow for the presence of treatment (exposure)-mediator interactions in the outcome regression model using counterfactual definitions of direct and indirect effects.

paramed allows continuous, binary or count outcomes, and continuous or binary mediators, and requires the user to specify an appropriate form for the regression models.

paramed provides estimates of the controlled direct effect, the natural direct effect, the natural indirect effect and the total effect with standard errors and confidence intervals derived using the delta method by default, with a bootstrap option also available.

Authors: Richard Emsley and Hanhua Liu

Further information: within Stata type ‘ssc describe paramed’

skbim (skewed bimodal data generator)

The program generates random numbers from a bimodal distribution.

The two unimodal distributions that make up the bimodal can be normal or skewed-normal (see sknor for more details). Different arguments can be input to the function, as specified by ‘option’.

Author: Evan Kontopantelis

Further information: within Stata type ‘ssc describe skbim’

sknor (skewed normal data generator)

The program generates random numbers from a skewed normal distribution (right-skew being the default).

Author: Evan Kontopantelis

Further information: within Stata type ‘ssc describe sknor’


We run regular seminars for staff and students at the University.


We organise half-day seminars in March and October with three external guest speakers.

We also run a seminar series on the first Monday of each month, and host occasional special seminars with guest speakers.

All of these seminars are free to attend.

If you’re interested in upcoming events, please get in touch.

Recordings of the events are available here:

April 2024 – Methods for Missing Data

Contact us

Email: biostatsenquiries@manchester.ac.uk
Tel: 44 (0) 161 306 8008

Room 1.307, Jean McFarlane Building
Oxford Road
M13 9PL, UK