Core B: Biostatistics and Bioinformatics Core
- Institution
- UNIVERSITY OF TX MD ANDERSON CAN CTR, TX
- Principal investigator
- Yisheng Li
- Funding
- $229K
- Duration
- 2025–2030
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Abstract
The primary goal of the University of Texas MD Anderson Cancer Center SPORE in Sarcoma is to reduce the morbidity and mortality from sarcomas through innovative translational research focused on immunologic and targeted treatments, based upon the underlying genetic aberrations contributing to the pathophysiology of the disease. This is a collaborative effort that will accomplish these aims by integrating laboratory and translational research. The Biostatistics and Bioinformatics Core will serve multiple needs for the planning and conduct of the SPORE's research. Based on a strong track record for providing biostatistical and computational support for translational research, this Core will be a comprehensive, multi-lateral resource for designing clinical and basic science experiments, performing statistical analyses, developing innovative statistical methodology, and sharing the data and publishing the research results generated from this Sarcoma SPORE. The Biostatistics and Bioinformatics Core will provide guidance in the design and conduct of clinical trials and other experiments arising from the ongoing research with the SPORE Projects, Developmental Research and Career Enhancement projects, and other Cores. This Core will also provide biostatistical and bioinformatic modeling, simulation techniques and data analyses needed by the Projects and other Cores to achieve their Specific Aims. The Biostatistics and Bioinformatics Core will generate statistical reports for all Projects, will assist project investigators in publishing and sharing scientific results, and will develop innovative statistical and bioinformatics methods pertinent to translational sarcoma studies including novel early phase adaptive clinical trial designs incorporating mechanistic, such as pharmacokinetics (PK) / pharmacodynamics (PD), information, as well as novel statistical methods for clinical trial data analysis.
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