Causal Inference for Assessing Effectiveness in Real World Data and Clinical Trials: A Practical Hands-on Workshop will take place in March 16-20, 2020, in Hall in Tirol, Austria. This 5-day course provides training on causal inference methods for the estimation of comparative effectiveness in observational real world data, randomized controlled trials (RCTs), and pragmatic trials with a specific focus on adjustment for time-varying confounding, selection bias, and treatment switching. The course is relevant for clinical researchers, statisticians, epidemiologists and health economists involved in health technology assessment, and for those reading the ICH E9 Addendum on Estimands and wanting to know how to estimate hypothetical estimands in RCTs.
The course includes graphical concepts (causal diagrams), structural approaches (target trial) and statistical methods (e.g., g-estimation, marginal structural models, and two-stage method). We combine theoretical concepts with practical applications using real world case examples. For more information please click here.
The early-bird registration deadline is 24th February 2020. Discounts apply for group bookings, or if you have previously participated in a Continuing Education Program Course on HTADS at UMIT.
Uwe Siebert, MD, MPH, MSc, ScD Professor of Public Health, UMIT
Nicholas Latimer, BSc, MSc, PhD Reader in Health Economics, Health Economics and Decision Science, University of Sheffield, UK
Ian White, MA, MSc, PhD MRC Professor of Statistical Methods for Medicine, Clinical Trials Unit, University College London, UK