Prof Ruth Keogh


United Kingdom

020 7927 2570
I am Professor of Biostatistics and Epidemiology in the Medical Statistics Department (Faculty of Epidemiology and Population Health) and Co-Director of the Centre for Data and Statistics Science for Health (DASH).


Department of Medical Statistics
Faculty of Epidemiology and Population Health


Centre for Data and Statistical Science for Health


I enjoy teaching for the MSc in Medical Statistics and the MSc in Health Data Science. I organise and teach the module on Survival Analysis. Other modules that I teach on include: Statistical Models for Discrete Outcomes; Analysis of Electronic Health Records; Statistics for Health Data Science.


My focus is on statistical methodology for the analysis of observational data, such as arising from patient registries and electronic health records, with a particular emphasis on causal inference methods and methods for analysis of time-to-event data. I am also involved in a number of areas of applied health research. I am especially interested in research in cystic fibrosis, and have worked for a number of years with data from national cystic fibrosis registries to gain insight into survival and the impacts of different treatments in cystic fibrosis. Other areas of applied work include cancer, organ transplantation, Covid-19, and kidney disease.

I am also interested in and have undertaken research in a number of other areas of statistical methodology including: methods for handling measurement error; methods for handling missing data; design and analysis of case-control studies.

I am funded by a UK Research and Innovation Future Leaders Fellowship (2019-2026) for a project entitled: "Evaluating effects of complex treatments in chronic disease using large observational datasets: From population to person".
Research Area
Statistical methods
Applied statistics (medical)
Electronic health records
Data science
Disease and Health Conditions
Cystic fibrosis

Selected Publications

Trial emulation with observational data in cystic fibrosis.
Davies, G; KEOGH, RH; Cystic Fibrosis Trial Emulation Network,;
The Lancet. Respiratory medicine
Median age of survival in the 80s! Is there sufficient evidence to believe it?
Stanojevic, S; Hamblett, N; Szczesniak, R; Cromwell, E; KEOGH, R;
Journal of cystic fibrosis : official journal of the European Cystic Fibrosis Society
Causal inference in survival analysis using longitudinal observational data: Sequential trials and marginal structural models.
KEOGH, RH; Gran, JM; Seaman, SR; Davies, G; Vansteelandt, S;
Statistics in medicine
Estimating distribution of length of stay in a multi-state model conditional on the pathway, with an application to patients hospitalised with Covid-19.
KEOGH, RH; Diaz-Ordaz, K; Jewell, NP; Semple, MG; De Wreede, LC; Putter, H; For the ISARIC4C Investigators,;
Lifetime data analysis
G-formula for causal inference via multiple imputation
BARTLETT, JW; Parra, CO; GRANGER, E; KEOGH, RH; Zwet, EW V; Daniel, RM;
Use of multiple imputation in supersampled nested case‐control and case‐cohort studies
Borgan, Ø; KEOGH, RH; Njøs, A;
Scandinavian Journal of Statistics
Mediation of the total effect of cystic fibrosis-related diabetes on mortality: A UK Cystic Fibrosis Registry cohort study.
TANNER, KT; Daniel, RM; Bilton, D; Simmonds, NJ; SHARPLES, LD; KEOGH, RH;
Diabetic medicine
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