The ASA’s Kansas Western-Missouri Chapter and the department of biostatistics and data science at the University of Kansas Medical Center hosted the 14th Annual Innovations in Design, Analysis, and Dissemination: Frontiers in Biostatistics and Data Science meeting April 22–23. This free meeting was conducted virtually and welcomed more than 140 researchers and trainees for a two-day event that featured a keynote lecture, journey lectures, a panel discussion, a virtual poster session, and invited and contributed oral presentations spanning a variety of biostatistics and data science topics.
Opening remarks were delivered by Ronald Wasserstein, ASA executive director and former two-term president of the Kansas Western-Missouri Chapter. In his opening remarks, Wasserstein emphasized the importance of membership in the ASA—especially for graduate students and trainees in the fields of statistics, biostatistics, and data sciences—and highlighted the value that comes with getting involved in your local ASA chapter.
The keynote address, “A Seat at the Table: The Key Role of Biostatistics and Data Science in the COVID-19 Pandemic,” was delivered by Jeffrey Morris, who underscored the central role our profession has played throughout the COVID-19 pandemic and included specific examples from his own work in this area. Morris is a professor of biostatistics, director of the biostatistics division at the Perelman School of Medicine at the University of Pennsylvania, and a fellow of the ASA.
In an era when misinformation and misinterpretation of data are commonplace, Morris provided a refreshing perspective on the need for biostatisticians to engage more in society, to have a seat at the table with policymakers and decision-makers, and to be visible to the media and public to ensure our understanding of the quantitative nuances in science and society are taken into account. A recording of Morris’s keynote address can be found on his blog, “COVID-19 Data Science.”
The conference also featured journey lectures by two distinguished members of our field: David Morganstein and Xihong Lin. Journey lectures are light-hearted, nontechnical talks by distinguished quantitative scientists. They describe their career paths, offer words of wisdom, and pass on lessons learned throughout their careers.
Lin is a professor of statistics and biostatistics at Harvard University and the Harvard School of Public Health, fellow of the ASA, and winner of the 2006 COPSS Presidents’ Award. He also has numerous other national/international honors and accolades.
Morganstein, now retired, was a vice president at Westat and the 2015 ASA president. He is also a fellow of the ASA.
Common themes across the two journey lectures included the importance in mentoring throughout one’s career, finding your niche, and the critical role of soft skills in our profession (e.g., oral and written communication, being a leader, working in a team, etc.).
For the first time in its 14-year history, the conference featured a panel discussion, the topic of which was “Careers in Biostatistics and Data Sciences.” Although the intended audience was graduate students and trainees, which represented approximately 50 percent of the meeting attendees, the discussion generated considerable interest among more established researchers, whose questions led to fruitful discussions.
The panel discussion featured four panelists, all early-career quantitative scientists within five years of receiving their terminal degree, but with unique and different career paths. They discussed their perspectives on topics that included how to be successful in the job market, what to expect during an interview, when to begin the job search/application process, the role of mentoring during training, and the role of mentoring in their current positions.
The meeting also featured a virtual poster session and 15–20-minute presentations from quantitative scientists in both academia and industry that spanned topics such as statistical methods for high-dimensional data, novel developments in machine learning, methods for estimating power in clinical trials with patient-reported outcomes, and the use of geographical support vector machines to analyze spatially correlated data.
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