Professor Ruth Keogh

BSc MSc DPhil

of Biostatistics & Epidemiology


Keppel Street
United Kingdom

020 7927 2570


I joined the Medical Statistics Department at LSHTM in 2012.

I studied Mathematics and Statistics at the University of Edinburgh, an MSc in Applied Statistics at the University of Oxford, and a DPhil in Medical Statistics/Epidemiology also in Oxford. Prior to coming to LSHTM I worked at the MRC Biostatistics Unit, Cambridge (2008-2012) and the Cancer Epidemiology Unit, University of Oxford (2006-2007). 


Faculty of Epidemiology and Population Health
Department of Medical Statistics


Centre for Statistical Methodology


I teach on the MSc in Medical Statistics. This includes organising a module on Survival Analysis, and also teaching modules on Generalized Linear Models and Advanced Research Methods

I enjoy supervising and advising a number of PhD students and act as the Research Degrees Coordinator for the Department of Medical Statistics. 


My research is funded by a UK Research and Innovation (UKRI) Future Leaders Fellowship on the topic of Evaluating effects of complex treatments in chronic disease using large observational datasets.

My interests lie in statistical methodology for the analysis of observational data, and in related applications, especially in epidemiology. I work especially in the area of cystic fibrosis, using data from national patient registries. 

The aim ofmy research programme is to adapt and evaluate statistical methodology needed to address questions about the effects of treatments on health outcomes using longitudinal observational data, with an emphasis on development of an approach based on formation of “sequential trials”. I am using these methods to tackle
crucial questions about treatment effects in two chronic disease areas, cystic fibrosis and type 2 diabetes, using data from the UK Cystic Fibrosis Registry and other observational data sources.

From 2015-2019 I was funded by a Medical Research Council Methodology Fellowship.

My research interests also include:
  • Methods for dynamically predicting survival using large patient databases using an approach called landmarking.
  • Use of multiple imputation to handle missing data in case-control studies and to make use of full cohort information in nested case-control and case-cohort studies.
  • Methods for handling missing data in Cox regression when there are time-varying exposure effects. 
  • Methods for correcting for the effects of exposure measurement error, especially in the field of nutritional epidemiology.
Research Area
Clinical databases
Statistical methods
Electronic health records

Selected Publications

Living risk prediction algorithm (QCOVID) for risk of hospital admission and mortality from coronavirus 19 in adults: national derivation and validation cohort study.
Clift AK; Coupland CAC; Keogh RH; Diaz-Ordaz K; Williamson E; Harrison EM; Hayward A; Hemingway H; Horby P; Mehta N
STRATOS guidance document on measurement error and misclassification of variables in observational epidemiology: Part 1-Basic theory and simple methods of adjustment.
Keogh RH; Shaw PA; Gustafson P; Carroll RJ; Deffner V; Dodd KW; Küchenhoff H; Tooze JA; Wallace MP; Kipnis V
Statistics in medicine
Dynamic survival prediction combining landmarking with a machine learning ensemble: Methodology and empirical comparison
Tanner KT; Sharples LD; Daniel RM; Keogh RH
Journal of the Royal Statistical Society Series A: Statistics in Society
Results from an online survey of adults with cystic fibrosis: Accessing and using life expectancy information.
Keogh RH; Bilton D; Cosgriff R; Kavanagh D; Rayner O; Sedgwick PM
PloS one
Dynamic predictive probabilities to monitor rapid cystic fibrosis disease progression.
Szczesniak RD; Su W; Brokamp C; Keogh RH; Pestian JP; Seid M; Diggle PJ; Clancy JP
Statistics in medicine
Dynamic Prediction of Survival in Cystic Fibrosis: A Landmarking Analysis Using UK Patient Registry Data.
Keogh RH; Seaman SR; Barrett JK; Taylor-Robinson D; Szczesniak R
Epidemiology (Cambridge, Mass)
Multiple imputation of missing data in nested case-control and case-cohort studies.
Keogh RH; Seaman SR; Bartlett JW; Wood AM
Analysis of Longitudinal Studies With Repeated Outcome Measures: Adjusting for Time-Dependent Confounding Using Conventional Methods.
Keogh RH; Daniel RM; VanderWeele TJ; Vansteelandt S
American journal of epidemiology
Observational study to estimate the changes in the effectiveness of bacillus Calmette-Guérin (BCG) vaccination with time since vaccination for preventing tuberculosis in the UK.
Mangtani P; Nguipdop-Djomo P; Keogh RH; Trinder L; Smith PG; Fine PE; Sterne J; Abubakar I; Vynnycky E; Watson J
Data Resource Profile: The UK Cystic Fibrosis Registry.
Taylor-Robinson D; Archangelidi O; Carr SB; Cosgriff R; Gunn E; Keogh RH; MacDougall A; Newsome S; Schlüter DK; Stanojevic S
International journal of epidemiology
See more Publications