The London School of Hygiene & Tropical Medicine (LSHTM) is renowned for its research, postgraduate studies and continuing education in public and global health. LSHTM has an international presence and collaborative ethos and is uniquely placed to help shape health policy and translate research findings into tangible impact. The specific mission of the LSHTM is “to improve health and health equity in the UK and worldwide; working in partnership to achieve excellence in public and global health research, education and translation of knowledge into policy and practice.” It is therefore an ideal research environment to conduct the QUALITOP research project, in order to reach the patients, clinicians and policy makers to improve the health status and quality of life of cancer patient at a large scale.
The Faculty of Epidemiology & Population Health (EPH) houses a large group of epidemiologists, demographers, statisticians and nutritionists working on issues of major public health importance in the UK and globally. EPH has approximately 400 staff members organised into four research departments. The Department of Non-Communicable Disease Epidemiology is the main focus of research on the epidemiology of non-communicable diseases at the London School of Hygiene & Tropical Medicine, including cancer research. The department has considerable methodological strength and experience, particularly in the conduct and analysis of large-scale longitudinal cohort and record linkage studies, which will be part of the of the tasks endorsed in this project for the identification of the determinants of side effects from routinely collected data.
More information can be found on the LSHTM website: https://www.lshtm.ac.uk/
The team involved in the project is part of the Inequalities in Cancer Outcomes Network (ICON) within the Department of Non-Communicable Disease Epidemiology. The research aims of this network are to describe and explain regional and socio-economic differences and inequalities in cancer outcomes. The results help policy-makers to target investment in cancer services to improve outcomes and reduce inequalities.
ICON is actively involved in methodological innovation in survival and time-to-event analysis, and participates in the European network CENSUR. The group has developed statistical software and other tools which have become widely used, and are freely accessible via our web-pages.
The ICON team involved in QUALITOP include Co-Investigators Dr Manuela Quaresma and Dr Sara Benitez Mahano, Database and Web Development Manager Mr Adrian Turculet, Research Group Manager Ms Yuki Alencar and full-time researcher Dr Ananya Malhotra.
Dr Ananya Malhotra is a research fellow at LSHTM with a background in statistics. Her interests primarily lie in the application of machine learning based methods. Her recent project involved derivation of a machine learning algorithm for early diagnosis of pancreatic cancer among patients using primary care electronic health records. Currently, for the QUALITOP WP5 Team, Ananya is looking at associations between characteristics of patients with advanced cancer, their treatments, the risk of experiencing treatment related adverse events and their subsequent quality of life, largely using self-reported data linked to national cancer registry.
Dr Clémence Leyrat, Assistant Professor in Medical Statistic, PhD in Biostatistics (2014). In the last 10 years, she has been developing an expertise in causal inference methods for the analysis of observational studies based on electronic health records. Her current work includes methodological development for the use of emulated trials from observational studies, with a recent application looking at the effect of surgery following lung cancer diagnosis on survival among older patients. Building on this expertise, she will be leading WP5, which aims to use causal methods to understand the causal associations between immunotherapy treatment, side effects and quality of life, using both routinely collected data and new prospective information.
Prof Bernard Rachet (MD, PhD), joined the LSHTM as a cancer epidemiologist in 2002. He leads a research programme on the inequalities in cancer care and cancer survival and has acquired a strong expertise in the analysis of population-based cancer registry data linked to multiple electronic health records datasets. This programme includes methodological development on the analysis of complex time-to-event data, on the analysis of such data with missing information, and how to disentangle causal pathways using mediation analysis. More recently, he has integrated machine learning techniques in some causal approaches to reduce the risk of model misspecification. He will bring this expertise (and his clinical background) primarily to support WP5, but also to help build the bridges between the clinical and theoretical work packages.