Close

Professor Ruth Keogh

BSc MSc DPhil

Professor
of Biostatistics & Epidemiology

Room
G36

LSHTM
Keppel Street
London
WC1E 7HT
United Kingdom

Tel.
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.

Affiliations

Department of Medical Statistics
Faculty of Epidemiology and Population Health

Centres

Centre for Statistical Methodology

Teaching

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 also on the MSc in Health Data Science.

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

Research

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.

The aim of my research programme is to apply and develop statistical causal inference methods for answering questions about the effects of treatments on health outcomes using observational data. I am especially interested on an approach based on mimicking a sequence of “hypothetical trials” within longitudinal data. 

I am using these methods to tackle crucial questions about treatment effects in cystic fibrosis (CF) using data from the UK Cystic Fibrosis Registry. This includes investigations of the impact of new precision medicines on long term outcomes, and of the impact of different treatment combinations.

I am also involved in Covid-19 research, including to understand the trajectories of patients hospitalised with Covid-19 and to evaluate the effectiveness of different treatment strategies.

My research interests also include:
  • Methods for the analysis of survival or time-to-event data in general.
  • Methods for dynamic prediction of survival using large patient databases using  landmarking, including using machine learning approaches.
  • Use of multiple imputation to handle missing data in case-control studies and time-to-event studies.
  • Methods for correcting for the effects of exposure measurement error.
Research Area
Clinical databases
Statistical methods
Electronic health records
Methodology
Modelling
Discipline
Pharmacoepidemiology
Epidemiology
Statistics

Selected Publications

Importance of patient bed pathways and length of stay differences in predicting COVID-19 bed occupancy in England
Leclerc QJ; Fuller NM; Keogh RH; Diaz-Ordaz K; Sekula R; Semple MG; Atkins KE; Procter SR; Knight GM
2021
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
2020
BMJ-BRITISH MEDICAL JOURNAL
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
2020
Statistics in medicine
Evaluation of a five-year predicted survival model for cystic fibrosis in later time periods.
Liou TG; Kartsonaki C; Keogh RH; Adler FR
2020
Scientific reports
Dynamic survival prediction combining landmarking with a machine learning ensemble: Methodology and empirical comparison
Tanner KT; Sharples LD; Daniel RM; Keogh RH
2020
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
2019
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
2019
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
2018
Epidemiology (Cambridge, Mass)
Multiple imputation of missing data in nested case-control and case-cohort studies.
Keogh RH; Seaman SR; Bartlett JW; Wood AM
2018
Biometrics
Investigating the effects of long-term dornase alfa use on lung function using registry data.
Newsome SJ; Daniel RM; Carr SB; Bilton D; Keogh RH
2018
Journal of cystic fibrosis
Analysis of Longitudinal Studies With Repeated Outcome Measures: Adjusting for Time-Dependent Confounding Using Conventional Methods.
Keogh RH; Daniel RM; VanderWeele TJ; Vansteelandt S
2017
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
2017
HEALTH TECHNOLOGY ASSESSMENT
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
2017
International journal of epidemiology
See more Publications