Annual Report 2008.pdf - SAMSI

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D. Consulted Individuals The individuals consulted for the broad selection of topics within programs and workshops were the members of two groups: • The Program Organizers, listed in Section II.A.1 • Members of the Advisory Committees, listed in Section II.J The specific topics that Program Working Groups chose to pursue were, in general, selected by the Working Group participants themselves, according to their combined interests. In almost all cases, however, a Program Leader headed each working group, so that specific research topics remained consistent with overall program goals. In Section II.E, the various Working Groups, and their members, are discussed.
4. Challenges in Dynamic Treatment and Multistage Decision-Making 4.1 Program Rationale 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, evidencebased 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 is 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. Computer scientists working the field of reinforcement learning and statisticians working in medical decision-making have quantified the properties that the state must possess; in addition, practical considerations associated with feasibility of collection, cost, and patient burden must be taken into account. Typically, a great deal of information is available at each decision point, and
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NSF Annual Report 2007-2008 Submitt
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Education and Outreach • 2-Day Wo
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C. Directorate’s Summary of Chall
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National Leadership and Involvement
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D. Synopsis of Developments in Rese
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the efficient treatment of complex
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c) RANDOM MEDIA Much of the work of
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2. Human Resource Development SAMSI
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[it] from time to time.” (M. Rose
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Undergraduate Workshops 2007-08 Sum
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3. Education The impact of SAMSI co
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The working group Climate and Weath
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oundary method as a means to impose
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assumed full responsibility for the
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I. Annual Progress Report The previ
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Kazi Ito NCSU Mathematics Ralph Smi
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2007-08 Sensor Networks Transition
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Environmental Sensor Networks Progr
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Yang Yu Zhang Zhong Zhu Jun Bin Yi
Page 39 and 40: Mattingly McKinley Minion Mitran Ng
Page 41 and 42: Cheng Choi Clark Cooley Das Das Del
Page 43 and 44: Ren Resnick Rios Cuirong Sidney Jes
Page 45 and 46: Rosenblum Rotnitzky Scharfstein Sun
Page 47 and 48: B. Postdoctoral Fellows This sectio
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Page 51 and 52: We consider modeling multiple risk
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Page 77 and 78: Jesus Rios Plans for 2008-10 I will
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Page 81 and 82: C. Graduate Student Participation 1
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Page 93 and 94: 4.5 Program Activities The program
Page 95 and 96: which held morning and afternoon se
Page 97 and 98: • B. Carlin (Biostatistics, Unive
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Page 101 and 102: 4.11 Summary of Exciting Research a
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Page 107 and 108: Cherokee Investment Partners, Const
Page 109 and 110: G. Publications and Technical Repor
Page 111 and 112: • Ma, C. “Construction of Non-G
Page 113 and 114: • Bayarri, M.J., J. Berger, F. Li
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Page 117 and 118: • Krishnapur, M., Zeitouni, O., R
Page 119 and 120: • Hou, S., K. Huang, K. Solna, an
Page 121 and 122: • Das, S., “Analyzing Fatal Dri
Page 123 and 124: H. Diversity Efforts SAMSI puts con
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Page 127 and 128: 2007-08 Random Media Opening Worksh
Page 129 and 130: 2. Affiliate Involvement 2.1 Backgr
Page 131 and 132: J. Advisory Committees Committee Na
Page 133 and 134: Institute directors agree to inform
Page 135 and 136: analysis of DNA and protein sequenc
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Carolina), Gareth Peters (New South
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actually replace individual analyse
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effect parameter of interest prior
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with structures that are not tempor
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about confidence in results. Furthe
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2. Stochastic Dynamics 2.1 Overview
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Michael Reed (Mathematics, Duke), G
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These two approaches, through the u
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• Li Lexin (North Carolina State
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At NCAR, Boulder, Colorado and orga
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SAMSI working groups. Participation
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certain distances, be used to captu
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subject to the l 1 constraint. Zou
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3. Breiman, L. (1996): Heuristics o
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enabled high virtual attendance by
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(b) Estimation of large random matr
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We have already discussed problems
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Appendix C. Workshop Participant Li
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Sanso Bruno Male University of Cali
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Compmod New Methods and Case Studie
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El Karoui Nourredine Male UC Berkel
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Education and Outreach Program SAMS
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Sutton Karyn Female NCSU Mathematic
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Lin Rongheng Male U Mass Biostatist
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Geometry and Statistics of Shape Sp
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Heo Giseon Female Hirani Anil Male
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Pennec Xavier Male Pike Brian Male
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Yasamin Saeid Male Yezzi Anthony Ma
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Daum Keith Male Idaho National Labo
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Langstaff John Male Laurey Terri Fe
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Sedransk Nell Female NISS Statistic
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Risk PPP Workshop Workshop Particip
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Rezaei Mahmoud Male Clemson Mathema
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Lysenko Natalia Female ETH Zurich S
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Biondini Gino Male State U New York
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Khatri Shilpa Female Courant Instit
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Spiller Elaine Female SAMSI Mathema
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Interface Workshop Workshop Partici
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Cheney Margaret Female Rensselaer P
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Agarwal Ankit Male U Kansas Enginee
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Lahiri Soumendra Male Texas A & M U
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Last Name SAMSI/CRSC Industrial Mat
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Reale- Levis Jim Male NCSU Engineer
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Gray William Male U. North Carolina
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Serban Nicoleta Female Georgia Inst
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Brotherson Temitope Female NJ Scien
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Frink Kaiem Male Elizabeth City Sta
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Jones Dana Female Bennett College f
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Munoz Breda Female RTI Internationa
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Smith Ralph Male NCSU Mathematics F
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Undergraduate Two-Day Workshop Part
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Schmidt Courtney Female Georgetown
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Schaeffer Amanda Female U Arizona M
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Presentation Chris Snyder, NCAR/MMM
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Chair of Afternoon Session Tom Sant
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1:00-2:15 Lunch Chair of Afternoon
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“Calibration of a Numerical Model
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model. The output is functional dat
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santner.1@osu.edu “Optimization o
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greatly from the active support of
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“Blind Kriging: a New Method for
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10:00-10:30 Break Characterizing th
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— hist_pie.m — hist_pie_exercis
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9:00 Presentations and Discussion
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11:25-11:55 From Population to Indi
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4:00 - 5:30 Shapes in Medical Imagi
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4:15 - 4:45 Discussion Panel Wednes
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dcremers@cs.uni-bonn.de “Shape In
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We will construct epsilon entropy e
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Department of Statistics flb@stat.w
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University of California Los Angele
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2:45 - 3:15 Break N. D. Shyamal Kum
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2:15 - 4:00 Working Group Formation
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and public health questions; little
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Howard Kunreuther University of Pen
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series. We propose a specific class
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Interface Tracking in Connection to
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Purdue University Center for Comput
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of the solution. Here we reveal a s
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of the grand challenges of science.
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numerical approach. Several example
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Biofilms are complicated, living co
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Knut Solna University of California
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This poster presents an analysis of
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X. Program on Risk Analysis, Extrem
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SPEAKER ABSTRACTS Chair: David Bank
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Thoughts on the Use of Decision Ana
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Bias Correction in Pharmaceutical R
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In the talk we will discuss some mo
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Stephen Sain, National Center for A
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Concha Gomez, University of Wiscons
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Iris Mack, Phat Math, Inc. and Flor
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8:15 - 8:45 Continental Breakfast a
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coordinate grids on the surface. To
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University of California, Irvine De
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overall a good agreement between im
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9:00-9:05 Alan Karr, National Insti
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3:30 - 4:00 Coffee Break Soumen Lah
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“Experiences in Developing an Eco
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Jennifer Hoeting Colorado State Uni
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kogle@uwyo.edu “Data-Model Integr
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“Compression and Analysis of Gold
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"A Cost-Efficient Approach to Wirel
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The main goal of a data collection
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Wednesday, January 23, 2008 Radisso
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“Robust Prediction Error Criterio
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one of the main sources of uncertai
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other. A core problem is how to inf
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"The Asymptotic Structure of Nearly
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SAMSI madar@post.tau.ac. “Bayesia
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11:30 - 12:00 Discussion Session 12
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garnier@math.jussieu.fr “Wave Pro
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has lagged. While it is quite clear
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Appendix E - Workshop Evaluations E
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E.2 Evaluation of Scientific Conten
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E.3 Evaluation of Staff Percent of
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E.5 Evaluation of Lodging Evaluatio
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