American Statistical Association
Dynamic treatment regimes (DTRs) are sequential decision rules that specify how to adapt the type, dosage and timing of treatment according to an individual patient’s time-varying characteristics. DTRs offer a framework for operationalizing the multistage decision-making in personalized clinical practice, thus providing an opportunity to improve it. They are particularly useful for the management of chronic disorders including mental illnesses, substance abuse, HIV infection, cancer, and so on. Constructing “evidence-based” DTRs from patient data requires implementation of cutting-edge study design and analysis tools. In this talk, I will discuss the key ideas governing the paradigm of DTRs, an emerging class of study designs called sequential multiple assignment randomized trial (SMART), and a regression-based analysis approach called Q-learning. The ideas will be illustrated through various study examples.
Dr. Bibhas Chakraborty is an assistant professor of Biostatistics in the Mailman School of Public Health, Columbia University. His research interests include dynamic treatment regimes, adaptive designs, design of multi-component intervention trials, causal inference, and statistical machine learning. Prior to joining Columbia, he completed his Ph.D. in 2009 from the Department of Statistics, University of Michigan, under the supervision of Prof. Susan A. Murphy, one of the pioneers of the field of dynamic treatment regimes.
|Date:||Tuesday, February 15, 2011|
|Time:||3:30 - 4:30 P.M.|
New York State Psychiatric Institute
1051 Riverside Drive
6th Floor Multipurpose Room (6602)
New York, New York