Designs for Dynamic Treatment Regimes

• Activity Number: 85
• Type: Invited
• Date/Time: Monday, August 8, 2005 : 8:30 AM to 10:20 AM
• Sponsor: ENAR
• Author(s): Philip W. Lavori*+ and Ree Dawson
• Companies: Stanford University and Frontier Science
• Keywords: clinical trials ; adaptive treatment ; randomization

Abstract

Clinical management of chronic disease requires an adaptive, dynamic treatment regime (DTR): rules for choosing the new treatment based on response to past treatments. Estimating and comparing the effects of DTRs from observed treatment and outcome requires that new treatments are assigned independently of potential future responses to treatment, conditional on the history of treatments and response to date ("sequential ignorability" (SI)). In longitudinal observational studies, SI must be assumed, while randomization of dynamic regimes can guarantee it. Using examples, we describe the simplest randomized experimental designs for comparing DTRs. We discuss how a dynamic treatment regime that starts with A and leads (sometimes) to B might be compared to either fixed treatment A or B. We discuss finding the optimal sequence of treatments in a DTR when there are several choices. We contrast two ways of incorporating randomization into studies to compare such regimes: baseline randomization among DTRs versus randomization at the decision points (sequentially randomized designs). We also discuss estimation and inference from both baseline randomized and sequentially randomized designs.