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Tim Morris, Principal Research Fellow in Statistical Methods

Tim is a statistician who works on the development, evaluation and understanding of statistical methods in health research.

After starting professional life as a trial statistician, Tim did a PhD on multiple imputation of missing data and then joined our methodology team. He now works mainly in the Analysis Methodology program of research, but he has projects crossing into the Design and Meta-Analysis methodology programs. His methodology work has an applied focus; that is, it aims to give applied researchers the understanding, confidence and tools they require for the design and analysis of their studies. Tim continues to work on the odd trial himself.

Tim is mainly known for his work on simulation studies, missing data (as co-author of a multiple imputation book) and covariate adjustment but works in several other methodological areas, listed below.

Roles: Co-chair of the MRC/UKRI Trials Methodology Research Partnership’s Statistical Analysis working group. Member of the Stratos Initiative’s Simulation and Visualisation panels. Associate editor at Statistics in Medicine. Tim is a keen Stata user and programmer acts as a scientific chair of the UK Stata Conference every second year. He acts as Stata code reviewer for Biometrical Journal’s Reproducible Research, and was a guest editor for Biometrical Journal’s special issue ‘Towards neutral comparison studies’.

Teaching: Tim is involved with teaching our MSc courses in Clinical Trials and Statistics for Clinical Trials but his main teaching contributions are through our short courses. In particular, Using simulation studies to evaluate statistical methods and Practical use of multiple imputation to handle missing data using Stata.


Selected publications

An up-to-date list of Tim’s publications can be found at Orcid and Google scholar.

T. P. Morris, I. R.White, and M. J. Crowther, “Using simulation studies to evaluate statistical methods”, Statistics in Medicine, vol. 38, no. 11, pp. 2074–2102, 2019. doi:10.1002/sim.8086.

T. P. Morris, A. S. Walker, E. J. Williamson, and I. R. White, “Planning a method for covariate adjustment in individually randomised trials: A practical guide”, Trials, vol. 23, no. 1, 2022. doi:10.1186/s13063-022-06097-z.

G. Heinze, A.-L. Boulesteix, M. Kammer, T. P. Morris, and I. R. White and, “Phases of methodological research in biostatistics—building the evidence base for new methods”, Biometrical Journal, 2023. doi: 10.1002/bimj.202200222. [Online]. Available: https://doi.org/10.1002/bimj.202200222.

R. Phillips, S. Cro, G. Wheeler, S. Bond, T. P. Morris, S. Creanor, C. Hewitt, S. Love, A. Lopes, I. Schlackow, C. Gamble, G. MacLennan, C. Habron, A. C. Gordon, N. Vergis, T. Li, R. Qureshi, C. C. Everett, J. Holmes, A. Kirkham, C. Peckitt, S. Pirrie, N. Ahmed, L. Collett, and V. Cornelius, “Visualising harms in publications of randomised controlled trials: Consensus and recommendations”, BMJ, vol. 377, e068983, 2022. doi:10.1136/bmj-2021-068983.

B. C. Kahan, T. P. Morris, B. Goulão, and J. Carpenter, “Estimands for factorial trials”, Statistics in Medicine, vol. 41, no. 22, pp. 4299–4310, 2022. doi:10.1002/sim.9510.

B. C. Kahan, T. P.Morris, I. R.White, C. D. Tweed, S. Cro, D. Dahly, T. M. Pham, H. Esmail, A. Babiker, and J. R. Carpenter, “Treatment estimands in clinical trials of patients hospitalised for covid-19: Ensuring trials ask the right questions”, BMC Medicine, vol. 18, 2020. doi: 10.1186/s12916-020-01737-0.





Research Interests

  • Simulation studies
  • Handling missing data
  • Sensitivity analysis
  • Covariate adjustment in RCTs
  • Estimands: definition and estimation
  • Design and analysis of non-inferiority trials
  • IPD meta-analysis and network meta-analysis
  • Stata and Mata


Research Areas



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