Master Class “Practical Ways to Use Contemporary Statistics”
This series, organized by Inserm U 942 and the two research networks related to it (FHU Promice and FHU Impec), offers insights into the application of statistical methods for the design and analysis of studies conducted by clinical researchers. Distinguished experts have offered lectures on topics such as « Correlated Data in Randomized Trials » and « The Infrequent Bayesian ». The series is scheduled from March to September 2022. You can find below the replay sessions (when available) and know more about the speakers.
Janet Turk Wittes, PhD is President Emerita of WCG Statistics Collaborative which she founded in 1990. Previously, she was Chief, Biostatistics Research Branch, National Heart, Lung, & Blood Institute (1983–89). In her work there on lipid lowering in cardiovascular studies, she began to think about the problem of correlated data in randomized trials. Since then, she has been involved in many trials dealing with ophthalmology, both in rodents and in people. Data from the two eyes within an individual are of course correlated. The 2006 monograph, “Statistical Monitoring of Clinical Trials – A Unified Approach” by Proschan, Lan, and Wittes, deals with sequential trials.
Her research has focused on design of randomized clinical trials, capture recapture methods in epidemiology, and sample size recalculation. She has served on a variety of advisory committees and data monitoring committees for government (NHLBI, the VA, and NCI) and industry. For the FDA, she was previously a member of the Cell Therapy Advisory Panel and is currently a member of the Circulatory Devices Advisory Panel. She was formerly Editor in Chief of Controlled Clinical Trials (1994-98). She is a Fellow of the American Statistical Association, the Society for Clinical Trials, the AAAS, and an elected member of the International Statistical Institute. From 1994-1998, she was Editor in Chief of Controlled Clinical Trials (1994-98). In 2006, she received the Janet L. Norwood Award for Outstanding Achievement by a Woman in the Statistical Sciences and in 2015 she received the W.J.Dixon Award for Excellence in Statistical Consulting, American Statistical Association (2015). She received her PhD in statistics from Harvard University in 1972.
David Schoenfeld, PhD is a Biostatistician at Massachusetts General Hospital. He is a Professor of Medicine at Harvard Medical School, and a Professor in the Department of Biostatistics at the Harvard School of Public Health. He has provided statistical support for investigators conducting clinical and laboratory research for more than 30 years. He is principal investigator for the Clinical Coordination Center for the ARDS Network, which represents over 30 hospitals and conducts multi-center clinical trials on Acute Respiratory Distress Syndrome and he is the principal statistician for the Northeast ALS consortium. Dr. Schoenfeld is a fellow of the American Statistical Association and has numerous papers in the statistical literature.
Dr. Schoenfeld developed the first omnibus goodness of fit test for the proportional hazards regression model, a model that is used extensively in clinical trials that have survival or time to progression as an endpoint. He also developed widely used graphical techniques for this model. Dr. Schoenfeld’s current research involves the application of causal inference to clinical trials, methods for the analysis of studies involving gene arrays and novel clinical trial designs in psychiatry and neurology.
Gary G. Koch, Ph.D., D.Sc. (Hon), is Professor of Biostatistics at The University of North Carolina at Chapel Hill, where he has served on the faculty since 1968. He received the BS in Mathematics and the MS in Industrial Engineering from The Ohio State University, the Ph.D. in Statistics from The University of North Carolina, and the D. Sc. (Honorary) from De Montfort University. His principal research interest is the development of statistical methodology for the analysis of categorical data and its corresponding applications to a wide range of settings in the health sciences. He has an extensive record of publication in statistics and in collaborative work in health sciences research.
The topics which are addressed by his publications include crossover studies, multi-center studies, longitudinal (multi-visit) studies, rank methods for ordered outcomes, covariance analysis, multiple comparisons, and adverse experience data analysis. He has previously served on the editorial boards of The American Statistician, Biometrics, Drug Information Journal, and Technometrics, and he is currently a member of the editorial boards for Statistics in Medicine and The Journal of Biopharmaceutical Statistics.
Nicolas GIRERD, MD, PhD, professor of Therapeutics, is a cardiologist and biostatistician currently deputy director of the Nancy Plurithematic Clinical Investigation Center (CIC)-Inserm, France. He completed his Cardiology training in Lyon, France, and completed his Masters degree in Clinical Epidemiology in Québec, Canada. He obtained a PhD in Biostatistics focused on treatment effect evaluation in survival models in Lyon, France.
He is currently the PI of several studies, including the REMI (Relationship Between Aldosterone and Cardiac Remodeling After Myocardial Infarction – NCT01109225), AHF-CORE (Acute Heart Failure – COngestion Repeated Evaluation – NCT03327532) and AMBUSH (AMBulatory UltraSound for Heart Failure Management – NCT04741711) studies. He is also the methodologist/biostatistician of several randomized clinical trials in the field of heart failure and/or MRA therapy (Effects of Induced Moderate HYPOthermia on Mortality in Cardiogenic Shock Patients Rescued by Veno-arterial ECMO (HYPO-ECMO) NCT02754193; Eplerenone in Patients Undergoing REnal Transplant (EPURE TRANSPLANT) NCT02490904).
His current research interests are mainly focused on the quantification and treatment of congestion in acute and chronic heart failure. He is the author of over 250 publications in international journals.
Julie Josse is a senior researcher in statistics and machine learning applied to health at Inria, a French research institute in digital sciences, and Professor at Ecole Polytechnique (Paris). She is an expert in the treatment of missing values (inference, multiple imputation, matrix completion, MNAR, supervised learning with missing values) and has created a website on the topic for users. Her research also focuses on causal inference techniques (causal inference with missing values, combining RCT and observational data, policy learning) for personalized medicine. Julie Josse is dedicated to reproducible research with R statistical software: she has developed packages including FactoMineR and missMDA to transfer her work.
Upcoming lecture on « Causal inference « .