Evidence-based medicine relies on using data to provide recommendations for effective treatment decisions for individual patients. However, in many settings, effects may be heterogeneous across individuals or, in the case of treatments given sequentially, heterogeneous within the same individuals over time as their health condition evolves. 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 precisionmedicine 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.
 
This course will provide an overview of precision medicine from the statistical perspective. Topics will include randomized trial designs for ATS, common estimation strategies for the single stage case (with a focus on applications to non-experimental data), and extensions of (some) estimation methods to the multi-stage setting. A basic knowledge of regression and some exposure to basic causal concepts (confounding, the propensity score) are assumed.