Biostatistics and Analysis

The validity of the research study results hinges on the rigor of the design, quality of collected data and application of appropriate statistical methods to answer the research questions.

Fortified with many decades of experience, the Oak Ridge Institute for Science and Education plans and performs statistical analyses for research studies to assess health risks and are tailored to answer specific research questions. The biostatistical analyses allow scientists to quantitatively assess the data collected for research, and the analyses produce relevant, reliable results from which others can draw defensible and insightful conclusions for strategic planning and relevant decision making.

Why are biostatistics and analytics important?

ORISE supports ongoing programs involving medical surveillance of workers and other populations, as well as occupational epidemiology and research. ORISE also performs special analyses of health conditions and specific groups of workers, as required by the U.S. Department of Energy.

To ensure credibility and accuracy, all analyses are preceded by rigorous and thorough data collection, data cleaning and verification, and state-of-the-art data management processes. ORISE’s approach utilizes accepted and proven statistical processes, and when appropriate, adapts methods for novel applications. Results of analyses and interpretations of data findings are disseminated through clear andengaging reports, publications, presentations, or online dashboards as befits the target audience.

The experts at ORISE possess a range of capabilities essential for appropriate statistical analyses, including:

  • Study design and implementation
  • Records collection, computerization, and evaluation of data for usability
  • Survey design and sampling
  • Data management, integration, and verification
  • Design, implementation and evaluation of epidemiological surveillance programs
  • Power and sample size calculations
  • Descriptive data analysis including advanced data visualizations
  • Data modeling using parametric and non-parametric methods
  • Advanced methods for survival analyses, dose response, and risk estimation
  • Interpretation and presentation of study results