Biostatistics Workshop 2018

Title: Causal questions and principled answers: a guide through the landscape for practising statisticians
Speakers: The Causal Inference Topic Group of the STRATOS (STRengthening Analytical Thinking for Observational Studies) Initiative, including Els Goetghebeur (Ghent University, Belgium), Saskia le Cessie (Leiden University, Netherlands), and Erica Moodie (McGill University, Montreal) 

Abstract: This course aims to support the practicing statistician in making it work: causal inference from observational data in a potential outcomes framework. How to choose among the many versions of exposure, the target population and indeed the estimand in a given setting? Different estimands serve different purposes while lending themselves (in)directly to natural constraints imposed on given datasets. We spend ample time exploring this in various contexts before explaining key features of estimation methods relying on either the `no unmeasured confounders’ assumption or the availability of “instrumental variables”. We review, apply and compare dedicated methods centered around outcome regression and stratification, inverse probability weighting and their double robust versions, when relying on the no unmeasured confounders assumption. We also discuss the value of two-stage least-squares estimation when exploiting an instrumental variable. We illustrate key results and estimation properties with published case studies, using either the original data or simulations. Hands on sessions will guide participants in using R or Stata with the data. The course assumes familiarity with regression. Participants should bring a laptop with a fully charged battery. Materials including datasets and a list of packages that should be downloaded and installed prior to the course will be made available through

The course will consist of four sessions:

Session 1: Causal estimands
Session 2: Estimation and inference under no unmeasured confounding: outcome regression, matching and stratification.

Session 3: Inverse probability weighting and doubly robust estimation
Session 4: Instrumental variables

The course instructors are part of the STRengthening Analytical Thinking for Observational Studies (STRATOS) initiative, which is a large collaboration of experts in many different areas of bio statistical research. The Causal Inference Topic Group of STRATOS is composed of: 

Professor Els Goetghebeur (chair), Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Belgium 
Els Goetghebeur obtained her PhD from the Limburgs Universitair Centrum (B). She held previous appointments at the London School of Hygiene and Tropical Medicine (UK), Maastricht University (NL) and the Harvard School of Public Health (US). Her research focus on problems in survival analysis shifted to causal inference when confronted with noncompliance in clinical trials. More recently she had the pleasure of working with Belgian and Swedish registers on quality of care. She is generally interested in working with electronic health records. She starts as c0-chief editor for Statistics in Medicine in 2018.

Associate Professor Ingeborg Waernbaum (co-chair), Department of Statistics, Umeå University, Sweden 
Ingeborg Waernbaum obtained her PhD in Statistics in 2008 from Umeå University. Since 2011 she is also Associate Professor at the Institute for Evaluation of Labour Market and Education Policy, IFAU, Uppsala, Sweden, where she works with the development of statistical methods for analysing registry data. Her main research interests are in causal inference with register data with a broad focus on topics related to confounder control such as covariate selection, model specification and robustness.

Professor Bianca de Stavola, GOS Institute of Child Health, University College London, UK
Bianca De Stavola recently joined UCL GOS ICH after 23 years at the London School of Hygiene and Tropical Medicine where she was Professor of Biostatistics in the Department of Medical Statistics and co-Director of the Centre for Statistical Methodology. Bianca received her PhD from Imperial College London and MSc from the London School of Economics and Political Sciences, after graduating in Statistical and Economic Sciences at Padua University (Italy). Bianca's main research activities involve the understanding, development and implementation of statistical methods for long-term longitudinal studies, with specific applications to life-course epidemiology. As these often involve causal enquiries, in particular related to understanding pathways towards disease development, mediation analysis is her main interest.

Associate Professor Erica Moodie, Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Canada 
Erica Moodie obtained her PhD in Biostatistics in 2006 from the University of Washington, before joining the Department of Epidemiology, Biostatistics, and Occupational Health at McGill University where she is now a William Dawson Scholar and an Associate Professor of Biostatistics. Her main research interests are in causal inference and longitudinal data with a focus on adaptive treatment strategies. She is an Elected Member of the International Statistical Institute, an Associate Editor of Biometrics and the Journal of the American Statistical Association. She holds a Chercheur-Boursier senior career award from the Fonds de recherche du Quebec-Sante.

Professor Saskia le Cessie, Department of Medical Statistics and Bioinformatics, Leiden University, the Netherlands
Saskia le Cessie is a statistician with a joint appointment at the Department of Clinical Epidemiology and the Department of Medical Statistics. She obtained her PhD in Medical Statistics at the University of Leiden and obtained a master in mathematics (with minor in informatics) at the University of Utrecht. She is a broadly oriented statistician and involved in several large epidemiological studies of the Department of Clinical Epidemiology. Her research interests are in epidemiological and statistical methods for observational studies, in particular instrumental variable analysis, mediation analysis, competing risks and causal modelling.