Professor Ruth Keogh
BSc MSc DPhil
of Biostatistics & Epidemiology
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).
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.