There is currently great interest in developing rules for recommending for or against treatment based on individual subject characteristics, especially in clinical settings where treatment is toxic, burdensome or costly, or where the condition being treated is thought to be heterogeneous. This course will focus on individual characteristics used to guide treatment decisions, sometimes called predictive, prescriptive, or treatment-selection biomarkers. These characteristics might be as simple as subject demographics, clinical characteristics or disease risk factors, or might be more complex, such as the results of gene expression technology or medical imaging. This course will describe preferred statistical approaches for discovering individual characteristics predicting treatment efficacy, and for developing and evaluating treatment rules. We will also discuss study design in this context. Students will receive training using R packages implementing the methodology.