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The vision of the Biostatistics and Bioinformatics Research Center is to achieve the overall goal of improving the health status in our community, by minimizing the number of healthy individuals who become diseased (red arrow in Figure 1), and maximizing the number of diseased individuals who are cured from a disease (green arrow in Figure 1).
This vision contributes towards the overall mission of Cedars-Sinai Medical Center, which is to improve the health status of its community. Figure 2 depicts how Cedars-Sinai Medical Center through a "Prevention Probe" and a "Treatment Probe" accomplishes its mission.
In the case where the disease is cancer, Prevention and Treatment probes are associated with detailed information flow (Figure 3). Each probe is composed of an Information acquisition component, where pertinent information from our community flows in, and an Intervention component, which list the actions taken by Cedars-Sinai Medical Center to improve the overall health in our community. Figure 3 also presents the three pillars of the Medical Center: Basic research, Prevention and Treatment.
The information acquisition component highlights the importance of the Medical Informatics Backbone, which collects, organizes and stores all incoming information from the community health indicators, one individual at a time. This information is fundamental to the design of experiments, studies and clinical trials (Figure 4).
The design and analyses of such experimental studies is a major responsibility of the Biostatistics and Bioinformatics Research Center in expanding the horizons of medical knowledge. The interactive nature among Basic Research, Prevention and Treatment is highlighted in Figure 4.
Design of experiments is a highly interactive activity where Investigators, Biostatisticians/Bioinformaticians and Database Analyst collaborate throughout the whole process. Figure 5 shows the breakdown of activities typically present in the design of experiments by research team members involved. The arrow connecting the “Formulation of New Hypotheses” to the “Translation into a Statistical Model” indicates the iterative nature of the process of acquiring scientific knowledge.