Great efforts have been made over the past 20 years in HIV-related research. The results have provided useful information regarding the patient's disease management based on the individual patient characteristics. However, most of such """"""""personalized medicine"""""""" strategies were obtained on an ad hoc basis. There are numerous examples of subgroup analyses that could not be validated. Novel quantitative methods are needed to systematically develop sound treatment strategies at a personal level. For example, on average, the highly active antiretroviral therapy (HAART) is a very potent therapy for treating HIV-infection. However, not everyone benefits from HAART. Using data from clinical trials and observational studies, it is important to establish prediction models that can identify future patients who would benefit from such potent therapies. One of the main themes for this current research plan is to systematically develop quantitative methods to make valid inferences for future patient responses. There are five research aims in the proposal. The first three specifically address the personalized medicine issues. The last two aims are to develop new methods for designing, monitoring, and analyzing HIV studies with event time responses, which are commonly used endpoints for evaluating new therapies. The PI has assembled a research team which has been very productive in developing theoretically sound and practical quantitative methods for designing, monitoring and analyzing HIV- related clinical trials and observational studies over the past funding periods. The PI and co-investigators are also senior statisticians for the Center for Biostatical AIDS Research Center at Harvard University. They are intimately involved in HIV studies via the AIDS Clinical Trails Group (ACTG) and Neurologic AIDS research Consortium (NARC). They have access to the data from past and current ACTG and NARC studies, and can directly apply the results from developed methodologies to the on-going and future HIV studies.
Quantitative methods will be developed for personalized medicine approaches for patient management of HIV disease using the data from clinical trials and observational studies. New statistical procedures will also be developed for designing, monitoring, and analyzing clinical trials for evaluating new therapies for HIV-infection.
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