Center for Informatics
To convert raw data into information leading to new knowledge, to help speed biomedical discoveries, disease prevention, and therapeutic interventions.
To contribute to City of Hope’s mission and strategic plan through the development and application of research information science methods and technologies, by:
- Supporting information management, integration, and synthesis across the translational research pipeline
- Collaborating in the design, computerization, analysis and reporting of basic, translational, clinical, and population research
- Facilitating the collection and delivery of accurate, complete electronic data for disease registries
- Ensuring high quality results by applying best practices and international standards
Division of Biostatistics
The Division of Biostatistics houses faculty and staff statisticians. Their major activities include: collaboration in basic, translational and clinical research; consulting on statistical questions in research; development of statistical methodology, teaching in the Irell & Manella Graduate School of Biological Sciences and in the Clinical Investigation Training Program. The division operates City of Hope's comprehensive cancer center's Biostatistics Core, and supports coordinating centers for the National Comprehensive Cancer Network, The NCRR Islet Cell Resource Centers and the California Cancer Consortium.
Division of Clinical Research Information Support
Division of Research Informatics
Clinical Protocol and Data Management
Clinical Protocol and Data Management (CPDM) provides centralized infrastructure and support for effective, efficient conduct of clinical research.
Division of Mathematical Oncology
The scientific challenge of Mathematical Oncology is how best to use models to quantify and predict which patients are most likely to benefit from specific mono or combination therapies, using data that can be obtained in the clinic. Although prognostic classifiers such as recursive partitioning analysis provide a patient-specific prediction of overall survival, this and similar statistical methods do not account for the evolution and heterogeneity of the cancer before, during, and after treatment. Viewing cancer as a dynamic system perturbed by therapy and the body’s immune system is crucial to understanding the disease course and how to tailor treatments to individual disease.