Dell Medical School

Biomedical Data Science Hub

Date stamp: 1.18.2023

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About: Mission, Areas of Inquiry, Principles

Mission

The mission of the BDS Hub is to serve Dell Medical School as a scholarly resource for collaborative application of quantitative research methods, as well as development of new such methods, in support of Dell Medical School’s missions in areas of biomedical, clinical, and population health investigation and innovation.

Areas of Inquiry

The primary areas of methodological activity include:

  • Clinical biostatistics, including clinical trials, health services research, and population health
  • Clinical research informatics
  • Bioinformatics

And, as needed:

  • Statistical genetics and genomics
  • Imaging analysis and informatics

Principles Driving the BDS Hub

Dell Medical School has the opportunity to rethink how health care is delivered, thus we should also rethink how data drives that process.

The BDS Hub seeks to employ and deploy data science faculty and staff geared towards collaborative scholarship, willing to invest in others’ projects via a long-term collaborative process, while seeking ways to advance their own scholarly agenda. As such, we expect to be involved soup-to-nuts: early on, from the conception of a project, all the way through the submission of the last manuscript. The goal is to work with Dell Med investigators to foster the evolution of their research careers that is mutually beneficial in terms of creative, rigorous, and funded scholarship that is well-rounded and minimizes missteps and lost time and effort.

Key principles:

  • Ground activity in modern principles of data science
  • Soup-to-nuts collaborative activity in investigations from project inception to final report, with PhD level BDS Hub members serving as Co-I, and MS level BDS Hub members serving as Biostatistician or Analyst
  • Develop novel quantitative methodologies independently initiated or stimulated by collaborative work
  • Foster scientific integrity for DMS research projects
  • Employ state-of-the-art quantitative methods
  • Leverage existing strengths on the broader UT campus
  • Serve as a methodological concierge to new projects, and pipeline to methodological resources elsewhere in DMS or across UT more broadly to support such project

Please review our policies listed below, or, for more details, see our current Strategic Plan.

Please contact aubrey.hooser@austin.utexas.edu with questions or if you would like some human interaction around your project/problem.

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People

Faculty and Staff

Affiliate Faculty


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    Policies for BDS Hub Collaborative Activity

    Scheduling

    We are a very lean group, so we need advance notice and time.

    For grant or funding applications with a deadline: (Note that in the below, "due" means when the science / programmatic part of the project is due to DMS or UT sponsored projects offices.)

    • Intervention with primary data collection: We need to know about and have a sense of the aims, nature, and scope of the project at least 8 weeks (and preferably 10) before it is due. This usually requires either a meeting or a well-constructed specific aims page along with some email exchanges. More is obviously better.
    • Observational study (no intervention) with primary data collection: We need to know about and have a sense of the aims, nature, and scope of the project at least 6 weeks (and preferably 8) before it is due.
    • Observational study with secondary data analysis only: We need to know about and have a sense of the aims, nature and scope of the project at least 6 weeks before it is due.

    We need a very good draft project plan, sufficient to develop statistical analysis plan and/or a power or sample size estimation issuing therefrom at least 6 (for primary data collection) or 5 (for secondary data analysis) weeks before the project is due.

    For manuscripts, analyses, and other projects, we will do our best to scope, schedule, and implement (and communicate about these items) on a case-by-case basis.

    Effort and Funding

    If the Biomedical Data Science Hub members are working on projects that will lead fairly directly to external funding, then the Biomedical Data Science Hub is expected to cover that effort with its funds, and for those individuals to also participate on the funded sequelae projects with effort funded by such projects.

    Faculty and staff effort on sponsored projects should have salary recovered at the same level as effort expended.

    Estimating biostatistics and biomedical informatics effort on sponsored projects:

    • As the researchers conducting the data science (biostatistics or bioinformatics) components of a project, those faculty and staff are in the best position to make an accurate estimate of the effort required, perhaps, in the case of staff, with the help of their supervisor.
    • In particular, it is not appropriate for the PI on a collaborative project to do this estimation in isolation of coordination with BDS Hub leadership.

    Generally, on collaborative projects, PhD level BDS Hub members should appear as Co-I, and MS members should appear as research staff, e.g., "Biostatistician".

    Acknowledgement and Authorship

    When BDS Hub faculty and staff contribute materially to scholarly work, co-authorship is expected according to guidelines in the biomedical and population health literature. A good source is The International Committee of Medical Journal Editors.

    When BDS Hub faculty and staff perform work on unfunded scholarly products (e.g., peer-reviewed manuscripts or public technical reports), please include the acknowledgement: "[Insert person('s)(s') name(s)] effort on this project was supported by core funds of the Dell Medical School at the University of Texas at Austin."

    Priorities for BDS Hub Effort

    For BDS Hub faculty and staff time and effort on projects that are not (yet) externally funded, in descending order of priority:

    1. Grant or contract applications

    2. Work leading to grant or contract applications, e.g.:

    • preliminary data analysis
    • manuscripts leading to preliminary data

    3. Study design or data analysis for junior or other "starting up" faculty

    4. Unfunded work on unfunded projects (this should be avoided)

    5. Unfunded work on funded projects (this should also be avoided)

    Note: If another UT or Dell Medical School unit wants to fund work internally, then the Biomedical Data Science Hub considers that to be “funded” and it would not figure into this calculus.

    Ground Rules for Data Reproducibility

    1. **We prefer to receive data in a statistical package, however if data come to us in Excel, data should Follow standards in article.** Data Organization in Spreadsheets

    2. When we get your data or other files, we are going to date stamp your file name with yymmdd, so it might get returned to you as such.

    3. Unless otherwise arranged we expect a “data dictionary” to be included with data.

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    Resources & Training

    Local Didactic Courses

    From Great Idea to Clear Results: A year long course offered in conjunction with Department of Women's Health, Dell Med Office of Research, and UTHealth School of Public Health in Austin for those who could benefit from learning research study design.

    Research Nuts & Bolts (offered by the Dell Med Office of Research) is a one-hour monthly forum focusing on topics related to execution of clinical and population research.

    Looking for a more specialized course on the UT-Austin campus? Contact the BDS Hub and we will try to help you locate one.

    General Texts and Asynchronous Training

    The Art of Data Science, by Roger D. Peng, and Elizabeth Matsui. Note you can pay as little as $0 if you decline the lecture videos and choose only the book option.

    The Data Science Salon: A Collaborative Learning Experience, by Roger D. Peng, Elizabeth Matsui, and Corinne Keet.

    Understanding data and statistics in the medical literature, by Jeffrey Leek, Lucy D'Agostino McGowan, and Elizabeth Matsui.

    Useful Articles

    Broman, K. W., & Woo, K. H. (2018). Data Organization in Spreadsheets. The American Statistician, 72(1), 2-10.

    Leek, J., & Peng, R. (2015). What is the question? Insights, 347(6228) 1314-1315.


    Have questions? Please email: aubrey.hooser@austin.utexas.edu.