Willi Sauerbrei
Dr. Sauerbrei is a senior statistician and professor in medical biometry at the Center for Medical Biometry and Medical Informatics, Medical Center - University of Freiburg, Germany. Since 1983 he has worked as an academic biostatistician with main interest in various issues of model building and in cancer research, with a particular concern for breast cancer. For 16 years he was the heas of a Clinical Trials Unit serving as a data and statistical center for national and international trials in oncology. With Patrick Royston he has developed the multivariable fractional polynomial approach (MFP) and extensions of it that is also subject of a book on multivariable model-building. MEthodological topics of interest include variable and function selection, model stability, treatment covariate interactions, time dependent effects in survival analysis, meta-analysis, reporting of research findings and high-dimensional data. He is the initiator and chair of the STRATOS (STRengthening Analytical Thinking for Observational Studies) intitiative.
Michal Abrahamowicz
Dr. Michal Abrahamowicz is a James McGill Professor of Biostatistics at McGill University, in Montreal, Canada. His statistical research aims at development and validation of new, flexible statistical methodology, with main focus on time-to-event (survival) analyses of prognostic and pharmaco-epidemiological studies. He has also developed new methods to control for different sources of bias in observational studies. His collaborative research includes arthritis, cardiovascular, cancer epidemiology. He is the Nominated Principal Investigator on a major grant from the Drug Safety & Effectiveness Network (DSEN) of the Canadian Institutes for Health Research that develops new methods for longitudinal studies of drug safety and comparative effectiveness, and includes >35 faculty members from 14 universities across Canada. He published >290 peer-reviewed papers, and supervised 19 PhD and 14 MSc students, and 7 post-doctoral fellows. He is the co-chair of the international STRATOS initiative. In 2010-14 he was a member of the Executive Committee of ISCB.
Marianne Huebner
Marianne Huebner is Associate Professor of Statistics and Probability at Michigan State University. After receiving a PhD in Applied Mathematics she also stayed at UC Berkeley, the Mathematical Sciences Research Institute in Berkeley, and worked at Mayo Clinic. She develops and applies statistical models motivated by scientific questions, specifically in the ares of colorectal, lung and breast cancer, and cardiovascular health. Her research interests include health outcomes research, electronic health records, survival analysis, initial data analyses, and statistical genomics. She is teaching both undergraduate and graduate level courses, some with online components. link
James Carpenter
James Carpenter is Professor of Medical Statistics at the London School of Hygiene and Tropical Medicine, and Programme Leader in Methodology at the MRC Clinical Trials Unit. His research interests include missing data and sensitivity analysis, meta-analysis and hierarchical modeling, with applications in clinical trials and epidemiology.
Email: James.Carpenter@lshtm.ac.uk. Web: www.missingdata.org.uk
Gary Collins
Dr. Gary Collins is an Associate Professor, Head of Prognosis Methodology and Deputy Director of the CSM. His research interests are primarily focused on aspects surrounding the development and validation multivariable prediction (prognostic) models (design and analysis) and he has publishes extensively in this area. He is also interested in the systematic review and appraisal of prognostic studies and is an author of the CHARMS Checklist for conducting systematic reviews of predicition modelling studies.
Gary is interested in the reporting of health research studies and in 2015 was invited to be a UK EQUATOR Centre Fellow. Along with Doug Altman, Karel Moons and Hans Reitsma, UMC Utrecht, the Netherlands, he led an international collaboration to produce the TRIPOD consensus guidance on issues to report when developing or validating (prognostic and diagnostic) prediction models. He is also a member of the GATHER working group, which is developing guidance for reporting global health estimates.
Gary has more than 120 peer-reviewed articles, editorials and commentaris, including 18 in the 'Big 6' general medical journals, and is the first or senior author of more than 50 articles. He is a Statistical Editor ('hanging commitee') for the British Medical Journal (since 2010), an Associate Editor for Research Integrity and Peer Review, and BMC Medical Research Methodology, and an Academic Editor for both PeerJ and PLoS One. link
Stephen Evans
Stephen Evans is Professor of Pharmacoepidemiology at the London School of Hygiene and Tropical Medicine (LSHTM) and is a medical statistician. Stephen was at the UK Medicines Control Agnecy (now the MHRA) from 1995 to 1999, and 2000-2002. While at the MCA he dealt with major safety issues such as HRT and breast cancer; vitamin K and childhood cancer; MMR and autism. After training in Physics and Computing he jouned The London Hospital in 1970 becoming Professor of Medical Statistics in 1990.
He is or has been on various editorial boards, including the British Jounral of Clinical Pharmacology and is an Associate Editor of Pharmaco-Epidemiology and Drug Safety; he was a section editor of Statistics in Medicine. He has been a statistical advisor to the British Medical Journal and a member of its editorial review committee for over 15 years. He is an independent expert member of the European Medicines Agency's committee that deals with drug safety (PRAC), and was a member of the WHO Global Advisory Committee on Vaccine Safety. He is an Honorary fellow of The Royal College of Physicians of London. link
Mitchell Gail
Dr. Gail is a Senior Investigator at the Biostatistics Branch, Division of Cancer Epidemiology and Genetics, NCI. He received an M.D. from Harvard Medical School and a Ph.D. in statistics from George Washington University. His work has included studies on the motility of cells in tissue culture, clinical trials of lung cancer treatments and preventive interventions for gastric cancer, assesement of cancer biomarkers, AIDS epidemiology, and models to project the risk of breast cancer. Dr. Gail's current research interests include statistical methods for epidemiologic studies, including studies of genetic factors, and models to predict the absolute risk of disease. He is also working on calibration and seasonal adjustment for multi-center molecular-epidemiologic studies.
Dr. Gail is a Fellow and former President of the Amerian Statistical Association, and a member of the U.S. National Academy of Medicine. He received the 2015 AACR-ACS Award for Excellence in Cancer Epidemiology and Prevention. link
Joerg Rahnenfuehrer
Joerg Rahnenfuehrer is professor for 'Statistical methods in genetics and chemometrics' at the Department of Statistics at TU Dortmund University, Germany, since 2007. He received a PhD in mathematics in 1999 from the University of Düsseldorf and worked as a postdoc in Vienna and at UC Berkeley and as a group leader at the Max-Planck-Institute for Informatics in Saarbrücken. His research group works on the development and application of statistical methods in bioinformatics and medicine. Major research topics are the analysis of high-dimensional genetic data for cancer diagnosis and therapy, toxicology, and proteomics. For model selection in the high-dimensional setting he investigates the integration of biological knowledge to improve prediction performance and the use of model based optimization to efficiently jointly perform model selection and algorithm hyperparameter tuning. link
Ewout Steyerberg
Dr. Ewout Steyerberg is professor of medical decision making at Erasmus MC, Rotterdam, the Netherlands. He finished his MSc education at the Dept of Medical Statistics of Leiden University (1991) and PhD at Erasmus University (1996), both in the direction of developing and validating prognostic models. Ewout is a methodological researcher primarily interested in prediction using advamces regression analysis and related techniques. Areas of interest include biostatistics, cost-effectiveness, decision analysis, comparative effectiveness and quality of care research, with applications in all major medical fields (cardiology, oncology, neurology, surgery, internal medicine, pediatrics). He published a text book "Clinical Prediction Models" (Springer 2009, link) and hundreds of more or less methodologically oriented and applied research papers. link
Andrew Vickers
Dr. Vickers' research falls into three broad areas: randomized trials, surgical outcomes research and molecular marker studies. A particular focus of his work is the detection and initial treatment of prostate cancer. Dr. Vickers has analyzed the 'learning curve' for radical prostatectomy. He is working on a series of studies demonstrating that a single measure of prostate specific antigen (PSA) taken in middle age can predict aggressive prostate cancer up to 30 years subsequently and had developed a statistical model for predicting the result of prostate biopsy based on a panel of markers. His work on randomized trials focuses on methods for integrating randomized trials into routine surgical practice so as to compare different approaches to surgery. As part of this work, he has pioneered the use of web-interfaces for obtaining quality of life data from patients recovering from radical prostatectomy and he is now core director of the Web Survey Core Facility. Dr. Vickers’ methodological research centers primarily on novel methods for assessing the clinical value of predictive tools. In particular, he has developed decision-analytic tools that can be directly applied to a data set, without the need for data gathering on patient preferences or utilities. Dr Vickers has a strong interest in teaching statistics. He is course leader for the Memorial Sloan Kettering biostatistics course, teaches on the undergraduate curriculum at Weill Medical College of Cornell University and is the author of the introductory textbook “What is a p-value anyway?”.