Biostatistical Analysis Assistance

Navigate Study Design and Biostatistics

The primary goal of the statistical data analysis is to utilize analytical approaches to extract the information and knowledge embedded in the data and providing appropriate uncertainty measures, answering prospective hypotheses laid out in the specific aims and providing exploratory hypothesis-generating analyses of rich biomedical data.  Methods employed range from the most basic descriptive statistics to state-of-the-art statistical methods that may involve multi-stage modeling, resampling methods, causal inference methods to adjust for potential biases in observational data, integrative models for multiple data types, predictive models for translational research, and innovative new methods developed by the world-class biostatistical faculty in our BERD.  Specific areas of methodological expertise are described in the Innovative Biostatistical Methods page. Approaches to ensure reproducible research including multiple testing adjustment, proper model validation, post-selection inference, and rigorous documentation of analysis scripts will be employed, and care will be taken to ensure researchers understand the appropriate level of evidence and uncertainty of their results so that the strength of conclusions are clearly communicated in subsequent publications.   More information on the latter item may be found under the Biostatistics and Epidemiology Program (BEMP). 

At times, when they have sufficient training to do so and access to appropriate software, investigators may perform analyses themselves with guidance from BERD core members, especially investigators on training grants.  Alternatively, a member of the BAC/DBMC, supported by the CTSA, is available to conduct limited analyses, with funding arrangements made for more extensive needs. This component of the BERD is crucial to the success of the core, both for investigators with limited expertise and/or resources, and for the application of more advanced and/or innovative methodology. Manuscript support is included as part of data analysis and includes composition of methods sections, production and/or review of tables and other statistical results presented, as well as interpretation of results and contribution to discussion sections of manuscripts as appropriate.