Invited Session Speakers
Opening Plenary: James Wason
Jonathan completed his PhD at LSHTM and subsequently held Research Fellow and Lecturer roles in the Medical Statistics department. He then took up a position in AstraZeneca's Statistical Innovation Group. Prior to rejoining LSHTM, he was a Reader in Statistics at the University of Bath. Jonathan's research has predominantly focused on the development on methods for handling missing data and measurement error. More recently, he has been working on the topic of 'estimands' in clinical trials, combining methods from causal inference and missing data.
Eleanor (Ellie) Van Vogt
Cluster Randomised Trials
Fan Li is assistant professor of Biostatistics at Yale School of Public Health. He obtained his PhD in Biostatistics from Duke in 2019 and is now faculty member at the Yale Center for Methods in Implementation and Prevention Science (CMIPS). His research aims to develop and improve statistical methods for designing and analyzing pragmatic clinical trials, including cluster-randomized and stepped-wedge trials. He has also developed causal inference methods for estimand-aligned analyses of randomized trials and observational studies, as well as techniques for improving internal and external validity to facilitate unconfounded treatment comparisons under different study designs.
Lisa Yelland is a Biostatistician Senior Research Fellow in the Women and Kids Theme and Co-Leader of the Biostatistics Unit at the South Australian Health and Medical Research Institute. Her research broadly aims to drive improvements in the health of mothers, babies and other populations by using innovative statistical approaches to improve trial design and analysis. She is currently investigating methods for designing and analysing clinical trials collecting partially clustered data, and methods for addressing imperfect stratification in the analysis of trials that utilise stratified randomisation.
Dr Laura B Balzer is an Associate Professor of Biostatistics at the University of California, Berkeley. Her expertise is in causal inference, machine learning, and messy real-world data. Dr. Balzer’s work addresses challenges in the design and analysis of both randomized trials and observational studies, including differential measurement and complex dependence. Her work is motivated by ongoing collaborations, which aim to eliminate HIV and improve community health in rural East Africa (e.g., searchendaids.com). Overall, Dr Balzer’s work is informed by cross-disciplinary, real-world problems and aims to ensure methodological advances in academia translate into real-world impact.
Innovative Trial Designs
Haiyan Zheng is a CRUK PRC Fellow in Statistical Methodology at the University of Cambridge. She is passionate about efficient design and analysis of clinical trials, particularly in the field of precision medicine. She currently leads a three-year research programme to develop statistical methods that (1) simultaneously evaluate treatment effects in multiple subgroups, and (2) allow for mid-course adaptations in master protocol trials.
Marta Bofill Roig
Marta Bofill Roig is a postdoctoral researcher at the Institute for Medical Statistics and Center for Medical Data Science at the Medical University of Vienna. Her research is currently focused on methods and software for the design and analysis of platform trials, with particular interest in the use of non-concurrent control data. Marta graduated in Mathematics and did a Master in Statistics and Operations Research. She has a PhD in Statistics and Operations Research from the Universitat Politècnica de Catalunya, during which she worked on trial designs with multiple endpoints.
David Robertson is a Senior Research Associate at the MRC Biostatistics Unit, University of Cambridge, where he has been based since 2013. His research focuses on the development of novel methodology for the design and analysis of adaptive clinical trials. David held a Biometrika Trust Research Fellowship from 2018 - 2021, which explored questions around error rate control for clinical trial designs that test multiple hypotheses simultaneously.
Miguel Hernán uses health data and causal inference methods to learn what works. As Director of the CAUSALab at Harvard, he and his collaborators repurpose real world data into scientific evidence for the prevention and treatment of infectious diseases, cancer, cardiovascular disease, and mental illness. Miguel is co-director of the Laboratory for Early Psychosis (LEAP) Center, principal investigator of the HIV-CAUSAL Collaboration, and co-director of the VA-CAUSAL Methods Core. As the Kolokotrones Professor of Biostatistics and Epidemiology, he teaches at the Harvard T.H. Chan School of Public Health and at the Harvard-MIT Division of Health Sciences and Technology. His free online course “Causal Diagrams” and book “Causal Inference: What If”, co-authored with James Robins, are widely used for the training of researchers.
Fan Li is a professor of Statistical Science, and Biostatistics and Bioinformatics at Duke University. She is the co-director of the Comparative Effectiveness Methodology Program at the Duke Clinical Research Institute. Her primary research interest is causal inference, with applications to comparative effectiveness research in health studies, clinical trials, and social science. She has developed the propensity score overlap weighting method. She also works on methods for missing data and Bayesian analysis. She is the editor for Social Science, Biostatistics and Policy of the Annals of Applied Statistics, and a fellow of the American Statistical Association.
Annabel Webb is an early career statistician in the final year of her PhD in biostatistics at Macquarie University, Australia. Her PhD research focuses on developing novel analysis methods for complex time-to-event data, with a particular emphasis on interval censored data, time-varying covariate models, joint modelling and dynamic prediction. Annabel also works as a Biostatistician at the Cerebral Palsy Alliance Research Institute where she contributes to a variety of research projects, including developing prediction models for the early detection of cerebral palsy, and the design of clinical trials for evaluating early interventions for children at risk of cerebral palsy.
Giorgos Bakoyannis is an Assistant Professor of Statistics at the Department of Statistics in Athens University of Economics and Business, Greece. He has received M.Sc. and Ph.D. degrees in Biostatistics from the National and Kapodistrian University of Athens in Greece. Dr. Bakoyannis has served as a postdoctoral research fellow (2014-2015), Assistant Professor (2015-2022), and Associate Professor (2022-2023) in the Department of Biostatistics and Health Data Science at Indiana University, U.S.A. His research interests include nonparametric and semiparametric statistical methodology for survival and competing risks data, multistate models, statistical methodology for precision medicine, missing data, and causal inference.
Ronald is the head of the biostatistics group at the Oxford University Clinical Research Unit (Ho Chi Minh City, Vietnam) and Associate Professor at the Oxford University. His research interests include models for complex time-to-event data and complex longitudinal data, prediction based on time-updated marker values and causal inference. He (co)-authored around 200 peer reviewed scientific articles and wrote the book "Data analysis with competing risks and multiple states". He co-authored a highly cited tutorial on competing risks and multi-state models and has taught courses and tutorials on the analysis of competing risks data in over 10 countries.
Health economics of trials
Rachael Morton is the Deputy Director at the NHMRC CTC and also leads the Health Economics and Health Technology Assessment teams. She specialises in trial-based and modelled economic evaluation, patient reported outcome measures (PROMs) and elicitation of patient preferences using discrete choice experiments. Her research incorporates patient-centred and economic outcomes into clinical trials of diagnostic tests, new treatments and models of care to facilitate policy decision-making on the basis of cost-effectiveness.