Summer 2007 Program on Challenges in Dynamic Treatment Regimes and Multistage Decision-Making
June 18-29, 2007

Research Foci
Description of Activities

• Tutorials
• Opening Workshop
• Working Groups
• Transition Workshop

Research Foci

The management of chronic disorders, such as mental illness, substance dependence, cancer, and HIV infection, presents considerable challenges. In particular the heterogeneity in response, the potential for relapse, burdensome treatments, and problems with adherence demand that treatment of these disorders involve a series of clinical decisions made over time. Decisions need to be made about when to change treatment dose or type and regarding which treatment should be used next. Indeed, clinicians routinely and freely tailor treatment to the characteristics of the individual patient with a goal of maximizing favorable outcomes for that patient. To a large extent the tailoring of sequences of treatments is based on clinical judgment and instinct rather than a formal, evidence-based process.

These realities have led to great interest in the development of so-called "dynamic treatment regimes" or "adaptive treatment strategies." A dynamic treatment regime is an explicit, operationalized series of decision rules specifying how treatment level and type should vary over time. The rule at each stage uses time-varying measurements of response, adherence, and other patient characteristics up to that point to determine the next treatment level and type to be administered, thereby tailoring treatment decisions to the patient. The objective in developing such multistage decision-making strategies is to improve patient outcomes over time.

Methodology for designing dynamic treatment regimes is an emerging area that presents challenges in two areas. First, experimental designs for collecting suitable data that can be used efficiently to develop dynamic regimes are required. Second, techniques for using these and other data to deduce the decision-making rules involved in a dynamic regime must be developed. In both areas, input from researchers in a variety of disciplines and collaborations among them will be critical.

Trials in which patients are randomized to different treatment options at each decision point have been proposed; however, little is known about when such trials should be conducted in lieu of the current approach of melding clinical judgment and expert opinion to formulate decision rules and using the standard two-group paradigm. An alternative approach is to conduct a series of randomized trials, as in agriculture and engineering; again, there is little guidance on how to implement this approach when the goal is to develop a dynamic regime.

Methods to make use of data in developing dynamic regimes involve complex considerations. The construction of optimized decision rules requires incorporating the effects of future decisions when evaluating present decisions, as is well-known to scientists working on improving multistage decision-making. Treatment given at any time may set a patient up for improved response to subsequent treatments or have delayed effects that either enhance or reduce effectiveness of subsequent treatments. The development of a dynamic regime hinges on how one operationalizes the relative importance of patient outcomes over time. Researchers who work on multistage decision problems in other contexts (robotics, artificial intelligence, control theory) readily recognize these types of issues. A key challenge is to determine how to collect sufficient information to ascertain the "state" of an individual insofar as making treatment decisions goes. Typically, a great deal of information is available at each decision point, and methods for feature extraction developed by statisticians and computer scientists are well-suited to this problem, but the focus on multistage decision-making rather than prediction requires evaluation of these methods from a different perspective.

Computational and inferential challenges arise in all of these endeavors; e.g., complexities of optimizing dynamic regimes can invalidate standard statistical inferential techniques, scientific considerations entail thinking beyond the standard loss functions familiar to statisticians, and the abundance of information at each decision point quickly leads to a "small n, large p" problem and the attendant computational issues. For some disorders, e.g., HIV infection, knowledge of the underlying within-subject biological has led to development of sophisticated mechanistic models for the processes governing disease progression and effect of treatment, which offer a scientific basis (via closed loop control methods) for designing dynamic regimes; however, this approach has not been widely explored or tested in samples of patients in this context.

This SAMSI summer program will bring this area to the attention of statistical and applied mathematical scientists, whose expertise is critical; jump-start the necessary methodological development; and nurture the necessary interdisciplinary collaboration and communication between statisticians/applied mathematicians and computer scientists and health and behavioral science researchers.

Program Leaders: Susan Murphy (University of Michigan), Daniel Scharfstein (Johns Hopkins Bloomberg School of Public Health), Joelle Pineau (McGill University); Local Scientific Coordinators: Marie Davidian and Butch Tsiatis (North Carolina State University).

Description of Activities

Tutorials (Monday, June 18 - Wednesday, June 20 at Radisson RTP):
Six tutorials will be held, two per day, to provide participants unfamiliar with the foundation necessary to assimilate more advanced developments.

Speakers

Monday, June 18, 2007

Tuesday, June 19, 2007

Wednesday, June 20, 2007

Opening Workshop (Thursday, June 21 - Friday, June 22 at Radisson RTP):
The workshop will feature one or two overview talks presenting the "big picture" of the methodological challenges followed by more advanced and targeted talks on research relevant to development of dynamic treatment regimes that build on the foundation provided by the tutorials. There will be talks the first full day and second morning, after which participants will be divided into discussion groups centered around four key areas that will form the basis for "brainstorming" by working groups during the next week of the program (below). The discussion groups will develop lists of important challenges and questions centered around their theme.

Thursday, June 21, 2007

Friday, June 22, 2007

Working Groups (Monday, June 25 - Wednesday, June 27 at SAMSI):
Four Working Groups will convene to discuss and prioritize challenges in their respective areas. Participants will identify the most pressing problems and outline modes of attack and specific research directions to be pursued. The proposed working group foci and potential lead participants are:

• Difficulties In Statistical Inference (Peter Bartlett, Susan Murphy, Sasha Rakhlin, Jamie Robins)
• Bayesian Approaches (Brad Carlin, Peter Thall)
• The Role of Mechanistic Models (Tom Banks, Victoria Chen, Marie Davidian, Daniel Rivera)
• Practical Challenges and Applications (Erica Moodie, Joelle Pineau, Butch Tsiatis)

Working Groups will meet daily according to a schedule that will allow participants to be involved with more than one group if desired.

Transition Workshop (Thursday, June 28 - Friday, June 29 at SAMSI):
Each Working Group will present their results, findings, and recommendations for the future to all Working Group members along with additional participants who may return for this final activity. Discussion will follow each group's presentation. These presentations and discussions will form the basis for a white paper outlining methodological challenges in the area of dynamic treatment regimes to be written by the Program Leaders for submission to a leading statistical or mathematical science journal, with input from participants.

Application

REGISTRATION IS NOW CLOSED

Further Information

For additional information about the program, send an email to [email protected]. Please send a letter describing your interest, along with a vita (if a new researcher), to the indicated e-mail address.

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