Dr Manuela Quaresma
Assistant Professor
London School of Hygiene & Tropical Medicine
15-17 Tavistock Place
London
WC1H 9SH
United Kingdom
I am an Assistant Professor of Statistics within the Inequalities in Cancer Outcomes Network (ICON) based in the Health Services Research and Policy Department (Faculty of Public Health and Policy).
I completed a part-time staff PhD on the topic "Population-based cancer survival at small area level: methodological developments" at the Department of Non-Communicable Disease Epidemiology, Faculty of Epidemiology and Population Health, LSHTM.
Affiliations
Teaching
I enjoy teaching for the MSc in Medical Statistics at LSHTM. I co-organise and teach the modules 'Introduction to Statistical Computing' and 'Survival Analysis'. I also teach SAS computing for the module 'Advanced Research Methods'.
Other MSc modules I was involved with in the past include: 'Foundations of Statistics (Probabilities)' and 'Bayesian Analysis' from the MSc in Medical Statistics, and the module 'Statistics for Epidemiology and Population Health' taught on several intensive MSc courses at LSHTM.
Between 2006-2019, I taught on the LSHTM short course 'Cancer Survival: Principles, Methods and Applications' covering topics such as, survival analysis, excess hazard modelling, age standardisation and data visualisation.
I tutor MSc students and supervise MSc projects for the MSc in Medical Statistics.
I co-supervised a PhD student from the Faculty of Epidemiology and Population Health at LSHTM (2018-2020).
Research
I am currently funded by the CRUK programme investigating "Inequalities in Cancer Care and Outcomes" (2020-2025). My focus is on statistical methodology for the analysis of cancer registration data augmented with various electronic health records, with a particular emphasis on cancer survival and excess hazard methods.
I am especially interested in the topics:
- Inequalities in cancer care and outcomes
- Cancer epidemiology
- Net survival methodology
- Excess hazard modelling
- Bayesian analysis
- Spatial analysis
- Quantitative evaluation of interventions
- Stata, R and SAS programming
- Data visualisation
- Cancer registration data
- Analysis of Electronic Health Records