The following are examples of current and planned CQSB research projects.
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Mathematical and statistical modeling to inform the design of HIV clinical trials
This project, for which CQSB Director Marie Davidian, a statistician; Co-Director H. T. Banks, an applied mathematician; and Associate Director Eric Rosenberg, an immunologist/infectious disease clinician, serve as investigators, is supported by a grant from the National Institute of Allergy and Infectious Diseases. One goal of the project is to use mathematical-statistical models that represent the interplay between the human immunodeficiency virus and the host immune system as a basis for designing realistic longitudinal treatment strategies for HIV infection that seek to slow the progression of the disease and clinical trials to study these strategies. A second goal is to design and carry out a clinical trial that will evaluate such strategies and collect intensive, detailed longitudinal data on the participants to be used to inform the development of more realistic models.
Based on longitudinal data collected by Dr. Rosenberg over several years at Massachusetts General Hospital on over 100 individuals acutely infected with HIV-1, this team has developed a complex mathematical-statistical framework to represent the dynamics of HIV within subjects in the infected population and to describe the variation in dynamics across the popluation. The team has demonstrated that these models can be used to predict the immunological and virological progression of the disease both for specific subjects and for the population. As a consequence, this model framework is an elegant tool that may be used to better inform the design of new HIV treatment strategies through the use of control theory and simulation. The team has used the models to design the clinical trial, which will start in January 2008 at Massachuesetts General Hospital.More information on this project is given in an article in Results, the NCSU publication focused on Research and Graduate Studies at the University.
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Generative Data Sets for Study of Dynamic Treatment Regimes
This project, which will begin in January 2008, exploits the simulation capability being developed in the HIV project above to provide a critical resource to the research community engaged in the study and formulation of so-called dynamic treatment regimes. This project is an outgrowth of the very successful Statistical and Applied Mathematical Sciences Institute (SAMSI) Summer 2007 Program on Challenges in Dynamic Treatment Regimes and Multistage Decision-Making, held in June 2007 in Research Triangle Park. Dynamic treatment regimes are sets of sets of sequential decision rules that specify how treatment should be given over time based on accumulating information on the patient up to the point of the next decision, 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 optimal dynamic treatment regimes based on data is an emerging area in statistics, applied mathematics, computer science, operations research, and engineering. In order to test and evaluate such methodology and benchmark competing methods, "test-bed," "generative" data sets that have been generated specifically for this purpose are an important resource. The CQSB is uniquely poised to produce a suite of such data sets to be made available to the research community.
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Mathematical-Statistical Modeling of Interactions Between Viral Infection and Host Immune Response in Organ Transplantation Recipients
Immediately after an organ is implanted into the body, the human immune system launches a massive immunologic attack to "reject" it, and thus immunosuppressive agents are given to transplantation recipients to eliminate organ rejection, which often must be taken for the rest of a patient's life. Although life saving, these drugs also suppress the immune system, leaving the patient vulnerable to further illness. This presents several key challenges for which mathematical-statistical modeling holds considerable promise. These include understanding the reemergence of the hepatitis C virus (HCV) and/or cytomegalovirus (CMV) and its prevention in patients who have undergone liver or kidney transplants; dissecting the interplay between common viruses, such as members of the herpes family, and the immune systems of transplant patients undergoing immunosuppressive anti-rejection treatment, who often endure such viral infections; and designing strategies for optimal use of immunosuppressive therapies in these patients. The CQSB is a natural setting for an integrated effort involving some of the world's top transplant scientists at Massachusetts General Hospital and Emory University and internationally known quantitative scientists at NCSU.
