Introduction to Personalized Medicine and Dynamic Treatment Regimes
Personalized medicine is focused on making treatment decisions for an individual patient based on his/her genetic/genomic, clinical, and other characteristics.  A popular perspective on achieving this goal is to focus on identifying subgroups of patients sharing certain characteristics who may benefit from a certain treatment and for whom new, targeted treatments may be developed.  Another is based on operationalizing clinical practice.  Clinicians make a series of treatment decisions over the course of a patient’s disease or disorder based on accruing information on the patient with the goal of achieving the “best” possible outcome for the patient.  A dynamic treatment regime is a list of sequential decision rules that formalizes this process.  Each rule corresponds to a key decision point in the disease/disorder progression and takes as input the information on the patient to that point and outputs the treatment that s/he should receive from among the available options.  A key step toward personalized medicine from the perspective of clinical practice is thus identifying the optimal dynamic treatment regime, that which would lead to the most favorable outcomes given the information available.  We describe a statistical framework based on the notion of potential outcomes in which the definition of an optimal treatment regime may be formalized.   This serves as background for  Butch Tsiatis’  talk, which follows immediately after the break.

Bio: Marie Davidian is William Neal Reynolds Professor of Statistics at North Carolina State University and Adjunct Professor of Biostatistics and Bioinformatics at Duke University.  She is a Fellow of the American Statistical Association (ASA), the Institute of Mathematical Statistics, and the American Association for the Advancement of Science, and is an elected member of the International Statistical Institute.  Marie has served as chair of grant review panels for the National Institutes of Health, as Coordinating and Executive Editor of Biometrics, as a member of US Food and Drug Administration Advisory Committees, and as 2004 President of the Eastern North American Region of the International Biometric Society and 2013 President of the ASA.  Her interests include analysis of longitudinal data, methods for design and analysis clinical trials and observational studies, methods for making statistical inference in the presence of missing or mismeasured data, and causal inference and dynamic treatment regimes.  She is co-author of the 1995 book “Nonlinear Models for Repeated Measurement Data” and is co-editor of the 2009 book “Longitudinal Data Analysis.”