Instructor
Dr. Erica E.M. Moodie
Erica E. M. Moodie is a Professor of Biostatistics and a William Dawson Scholar. She obtained her MPhil in Epidemiology in 2001 from the University of Cambridge and a PhD in Biostatistics in 2006 from the University of Washington, before joining McGill University. 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, and an Associate Editor of Biometrics. She holds a Chercheur-Boursier senior career award from the Fonds de recherche du Quebec-Sante.
Course description
Evidence-based medicine relies on using data to provide recommendations for effective treatment or prevention decisions. However, in many settings, effects may be heterogeneous across individuals, and within individuals over time. Healthcare providers are faced with the daunting task of making sequential therapeutic decisions having seen few patients with a given clinical history. Adaptive treatment strategies (ATS) operationalize the sequential decision-making process in the precision medicine paradigm, offering statisticians principled estimation tools that can be used to incorporate patient’s characteristics into a clinical decision-making framework so as to adapt the type, dosage or timing of intervention according to patients’ evolving needs.
Target audience
Statisticians familiar with regression methods.
Syllabus
This half-day course will provide an overview of precision medicine from the statistical perspective.
Topics:
- Relevant data sources
- Common estimation methods for the single stage setting
- Q-learning
- G-estimation
- dWOLS
- Inverse probability weighting
- Extension to multi-stage setting
Required software
Some brief references to R will be made, but there are no hands-on components and students unfamiliar with R will not be at a disadvantage.
