Department of Information Sciences

The Department of Information Sciences collaborates in numerous facets of both clinical and basic science research throughout the Medical Center and Beckman Research Institute, including study design, data quality and training, and computational statistical analysis.
Major activities include collaborations in clinical, laboratory, and epidemiological research; development of statistical methodologies; teaching in the and in the and development of innovative information systems for research. The faculty members within the department participate as co-investigators on City of Hope grants, in addition to conducting their own research.

Our Vision

To convert raw data into information leading to new knowledge, to help speed biomedical discoveries, disease prevention, and therapeutic interventions.

Our Mission

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

The Division of Clinical Research Information Support provides expertise in training and continuing education of clinical research staff, data quality assurance, data collection for observational databases, quality assurance, multi-center coordination for treatment and observational protocols, data collection for disease registries and transplant procedures at City of Hope, cancer registry, data entry, and Clinical Trials Online. The Division operates the protocol data management component of the Cancer Center’s Clinical Protocol & Data Management.

Division of Research Informatics

The Division of Research Informatics includes systems analysts, application developers/software engineers, database administrators/architects, Electronic Data Capture/TeleForms group, and decision support services. The staff within RI is specially trained to focus on the information systems requirements unique to the research domain. RI combines the administrative and clinical care data collected during the normal course of patient care with protocol-specific data collected for research purposes and makes it available for use in outcome assessment and analysis. The Division also operates the clinical research informatics component of the Cancer Center’s Clinical Protocol & Data Management.
Through the Biostatistics Core and Clinical Protocol & Data Management Shared Resource, the Department of Information Sciences faculty and staff follow NCI guidelines to provide support to cancer center members and City of Hope investigators.

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

Mathematical Oncology is the science of oncology that uses mathematics as the means of discovery. The goal of the Division of Mathematical Oncology is to translate mathematics, physics and evolution-based research to clinical care.

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.