Dr Ruth Keogh BSc MSc DPhil
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I studied Mathematics and Statistics at the University of Edinburgh, graduating in 2002. I subsequently studied for an MSc in Applied Statistics at the University of Oxford, and a DPhil in Medical Statistics/Epidemiology also in Oxford, which I completed in 2006.
I joined the Medical Statistics Department at LSHTM in June 2012. Previously I worked at the MRC Biostatistics Unit, Cambridge (2008-2012) and the Cancer Epidemiology Unit, University of Oxford (2006-2007).
I teach on the MSc in Medical Statistics. I organise and teach on a module on Survival Analysis. I also teach on the Generalized Linear Models module and on the Advanced Research Methods module.
I enjoy supervising and advising a number of PhD students and act as the Research Degrees Co-ordinator for the Department of Medical Statistics.
I am funded by a Medical Research Council Methodology Fellowship.
I am an interested in both statistical methodology and applications.
Dynamic prediction of survival and Cystic Fibrosis
My main current research interest is in methods for predicting survival using large patient databases using an approach called landmarking. This is the main focus on my MRC Methdodology Fellowship.
This work is motived by an aim of making dynamic predictions of survival and times to other events for people with Cystic Fibrosis using data from the UK Cystic Fibrosis Patient Registry and the US Cystic Fibrosis Foundation Patient Registry. My prediction models make use of up-to-date measures on an individual’s health status to provide personalised predictions.
A particular aim of this research is to contribute to improving how information on life expectancy is presented to people with CF. To inform this I have developed an online questionnaire entitled: “Online survey to gain understanding of what people with cystic fibrosis aged 16+ would like to learn about their life expectancy and other outcomes”. More information on this work can be found here: http://blogs.lshtm.ac.uk/ruthkeogh
I am a co-investigator on the study “Cystic Fibrosis Epidemiological Network (CF-EpiNet) – Harnessing Data to Improve Lives”, which is funded by the Cystic Fibrosis Trust Strategic Research Centre grant. This funds a number of researchers and PhD students at several UK institutions.
I am also a consultant on two US studies in CF: “Multicenter validation of predictive sputum biomarkers in CF” and “Explanatory models of CF Survival, Infection and Intermediate Clinical Outcomes”.
Other research interests: Areas of application
I continue to be involved in studies in nutritional epidemiology, particularly with colleagues from the University of Cambridge using the EPIC-Norfolk cohort.
I am also the statistician on a study of the long term efficacy of the BCG vaccine for TB.
Estimating time-varying exposure-outcome associations using case-control data: logistic and case-cohort analyses.
Keogh, R.H. ; Mangtani, P. ; Rodrigues, L. ; Nguipdop Djomo, P. ;
BMC Med Res Methodol, 2016; 16(1):2
Statistical issues related to dietary intake as the response variable in intervention trials.
Keogh, R.H. ; Carroll, R.J. ; Tooze, J.A. ; Kirkpatrick, S.I. ; Freedman, L.S. ;
Stat Med, 2016;
Bayesian correction for covariate measurement error: A frequentist evaluation and comparison with regression calibration.
Bartlett, J.W. ; Keogh, R.H. ;
Stat Methods Med Res, 2016;
Nested case-control studies: should one break the matching?
Borgan, Ø.; Keogh, R.;
Lifetime Data Anal, 2015; 21(4):517-41
Handling missing data in matched case-control studies using multiple imputation.
Seaman, S.R. ; Keogh, R.H. ;
A toolkit for measurement error correction, with a focus on nutritional epidemiology.
Keogh, R.H.; White, I.R.;
Stat Med, 2014; 33(12):2137-55
Keogh, R.H.; Cox, D.R.
Cambridge University Press (New York, USA) 2014
Using full-cohort data in nested case-control and case-cohort studies by multiple imputation.
Keogh, R.H.; White, I.R.;
Stat Med, 2013; 32: 4021-4043
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