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Despite the critical role that quantitative scientists play in biomedical research, graduate programs in quantitative fields often focus on technical and methodological skills, not on collaborative and leadership skills. In this study, we evaluate the importance of team science skills among collaborative biostatisticians for the purpose of identifying training opportunities to build a skilled workforce of quantitative team scientists.
Our workgroup described 16 essential skills for collaborative biostatisticians. Collaborative biostatisticians were surveyed to assess the relative importance of these skills in their current work. The importance of each skill is summarized overall and compared across career stages, highest degrees earned, and job sectors.
Survey respondents were 343 collaborative biostatisticians spanning career stages (early: 24.2%, mid: 33.8%, late: 42.0%) and job sectors (academia: 69.4%, industry: 22.2%, government: 4.4%, self-employed: 4.1%). All 16 skills were rated as at least somewhat important by > 89.0% of respondents. Significant heterogeneity in importance by career stage and by highest degree earned was identified for several skills. Two skills (“regulatory requirements” and “databases, data sources, and data collection tools”) were more likely to be rated as absolutely essential by those working in industry (36.5%, 65.8%, respectively) than by those in academia (19.6%, 51.3%, respectively). Three additional skills were identified as important by survey respondents, for a total of 19 collaborative skills.
We identified 19 team science skills that are important to the work of collaborative biostatisticians, laying the groundwork for enhancing graduate programs and establishing effective on-the-job training initiatives to meet workforce needs.
A recent literature review revealed no studies that explored teams that used an explicit theoretical framework for multiteam systems in academic settings, such as the increasingly important multi-institutional cross-disciplinary translational team (MCTT) form. We conducted an exploratory 30-interview grounded theory study over two rounds to analyze participants’ experiences from three universities who assembled an MCTT in order to pursue a complex grant proposal related to research on post-acute sequelae of COVID-19, also called “long COVID.” This article considers activities beginning with preliminary discussions among principal investigators through grant writing and submission, and completion of reviews by the National Center for Advancing Translational Sciences, which resulted in the proposal not being scored.
There were two stages to this interview study with MCTT members: pre-submission, and post-decision. Round one focused on the process of developing structures to collaborate on proposal writing and assembly, whereas round two focused on evaluation of the complete process. A total of 15 participants agreed to be interviewed in each round.
The first round of interviews was conducted prior to submission and explored issues during proposal writing, including (1) importance of the topic; (2) meaning and perception of “team” within the MCTT context; and (3) leadership at different levels of the team. The second round explored best practices-related issues including (1) leadership and design; (2) specific proposal assembly tasks; (3) communication; and (4) critical events.
We conclude with suggestions for developing best practices for assembling MCTTs involving multi-institutional teams.
Clinical trials provide the “gold standard” evidence for advancing the practice of medicine, even as they evolve to integrate real-world data sources. Modern clinical trials are increasingly incorporating real-world data sources – data not intended for research and often collected in free-living contexts. We refer to trials that incorporate real-world data sources as real-world trials. Such trials may have the potential to enhance the generalizability of findings, facilitate pragmatic study designs, and evaluate real-world effectiveness. However, key differences in the design, conduct, and implementation of real-world vs traditional trials have ramifications in data management that can threaten their desired rigor.
Three examples of real-world trials that leverage different types of data sources – wearables, medical devices, and electronic health records are described. Key insights applicable to all three trials in their relationship to Data and Safety Monitoring Boards (DSMBs) are derived.
Insight and recommendations are given on four topic areas: A. Charge of the DSMB; B. Composition of the DSMB; C. Pre-launch Activities; and D. Post-launch Activities. We recommend stronger and additional focus on data integrity.
Clinical trials can benefit from incorporating real-world data sources, potentially increasing the generalizability of findings and overall trial scale and efficiency. The data, however, present a level of informatic complexity that relies heavily on a robust data science infrastructure. The nature of monitoring the data and safety must evolve to adapt to new trial scenarios to protect the rigor of clinical trials.
OBJECTIVES/GOALS: This report evaluates participants’experiences from three universities who assembled a complex grant proposal related to research on post-acute sequala of COVID-19 (PASC), also called long COVID. Activities reviewed ranged from the assembly of the team to responses to reviews by the National Center for Advancing Translational Sciences (NCATS). METHODS/STUDY POPULATION: Data were collected by means of semi-structured interviews, conducted and recorded on Zoom, with a sample of 15 scientists and staff both during proposal assembly and following proposal review. The sample comprised 40% of the total team equally selected from the 3 universities, The interview protocol was reviewed by the IRB at UTMB and the interviews were recorded on Zoom, and analyzed by means of the constant comparative strategy in the grounded theory method of qualitative research. Given the relatively small number of interviews in this project, we paid special attention to preserving the confidentiality of respondents. Only the verbal tracks of the interviews were professionally transcribed. Respondents were asked to suggest changes for future inter-organizational proposals. RESULTS/ANTICIPATED RESULTS: FIRST INTERVIEWS *LEADERSHIP: The scope of leadership opportunities was expanded as sub-teams in specific areas such as community engagement were formed. *TEAM: Each university’s community engagement team specializes in a different ethnic clientele, precluding a singular statement for the proposal. SECOND INTERVIEWS *LEADERSHIP: Staff members noted that the team concept too easily evolved into a bureaucratic format, resulting in less negotiation and more direction. *ASSEMBLY TASKS: The Writing Team turned out to be one of the most critical staff teams. *COMMUNICATION: The behavioral scientists in community engagement do not necessarily share paradigms (e.g., public health, psychology, and social work). They had difficulty generating productive communication and a unified statement for the proposal. DISCUSSION/SIGNIFICANCE: The scientists, as a group, suggested that future proposals should focus on one general topic, such as the microbiome, as opposed to attempting to integrate widely divergent interests. The scientists as a group should decide a priori whether to treat innovative ideas such as machine learning science as a science or a service.
OBJECTIVES/GOALS: The overall goal is to provide tips and suggestions for designing a summer internship program for undergraduates at an institution that only offers advanced degrees. This poster seeks to highlight what worked well and items that can be improved upon from a program that ran for the first time at the University of Texas Medical Branch in Summer 2022. METHODS/STUDY POPULATION: In February 2022, we opened admissions to a summer internship program whose goals are to expose students to statistical design and analysis problems from real research, while providing them with the conceptual and technical tools to address these problems. As such, students should be able 1) to assess study designs for strengths and weaknesses in addressing specific biomedical questions, 2) to use the tools of statistical modeling, data science, and hypothesis testing along with state-of-the-art statistical software to work on problems as well as produce results and conclusions for these problems, and 3) to understand how developing these skills can lead to a wide variety of career opportunities both inside and outside of academia. We ultimately recruited 12 participants in the first summer. RESULTS/ANTICIPATED RESULTS: Based on feedback from participants, we have identified recruitment efforts that worked (and those that need improvement), teaching methods that worked (as well as those that need improving), and ideas for career development sessions (based on questions generated from current participants). Each participant was paired with another student on a research project. Each research project had both a biostatistics faculty mentor as well as a basic science/clinical faculty mentor. We will discuss what worked/what needs improvement from this sort of situation as well. The program culminated in a poster session which was very well received by participants and faculty alike. DISCUSSION/SIGNIFICANCE: This internship program is significant because developing a workforce knowledgeable in biostatistics and data science has been increasingly in demand to improve the efficiency and reliability of biomedical innovation. My providing guidance to others hoping to provide similar programs, we aim to develop such skills in the incoming workforce.
Rigorous scientific review of research protocols is critical to making funding decisions, and to the protection of both human and non-human research participants. Given the increasing complexity of research designs and data analysis methods, quantitative experts, such as biostatisticians, play an essential role in evaluating the rigor and reproducibility of proposed methods. However, there is a common misconception that a statistician’s input is relevant only to sample size/power and statistical analysis sections of a protocol. The comprehensive nature of a biostatistical review coupled with limited guidance on key components of protocol review motived this work. Members of the Biostatistics, Epidemiology, and Research Design Special Interest Group of the Association for Clinical and Translational Science used a consensus approach to identify the elements of research protocols that a biostatistician should consider in a review, and provide specific guidance on how each element should be reviewed. We present the resulting review framework as an educational tool and guideline for biostatisticians navigating review boards and panels. We briefly describe the approach to developing the framework, and we provide a comprehensive checklist and guidance on review of each protocol element. We posit that the biostatistical reviewer, through their breadth of engagement across multiple disciplines and experience with a range of research designs, can and should contribute significantly beyond review of the statistical analysis plan and sample size justification. Through careful scientific review, we hope to prevent excess resource expenditure and risk to humans and animals on poorly planned studies.
Machine learning (ML) provides the ability to examine massive datasets and uncover patterns within data without relying on a priori assumptions such as specific variable associations, linearity in relationships, or prespecified statistical interactions. However, the application of ML to healthcare data has been met with mixed results, especially when using administrative datasets such as the electronic health record. The black box nature of many ML algorithms contributes to an erroneous assumption that these algorithms can overcome major data issues inherent in large administrative healthcare data. As with other research endeavors, good data and analytic design is crucial to ML-based studies. In this paper, we will provide an overview of common misconceptions for ML, the corresponding truths, and suggestions for incorporating these methods into healthcare research while maintaining a sound study design.
Statistical literacy is essential in clinical and translational science (CTS). Statistical competencies have been published to guide coursework design and selection for graduate students in CTS. Here, we describe common elements of graduate curricula for CTS and identify gaps in the statistical competencies.
We surveyed statistics educators using e-mail solicitation sent through four professional organizations. Respondents rated the degree to which 24 educational statistical competencies were included in required and elective coursework in doctoral-level and master’s-level programs for CTS learners. We report competency results from institutions with Clinical and Translational Science Awards (CTSAs), reflecting institutions that have invested in CTS training.
There were 24 CTSA-funded respondents representing 13 doctoral-level programs and 23 master’s-level programs. For doctoral-level programs, competencies covered extensively in required coursework for all doctoral-level programs were basic principles of probability and hypothesis testing, understanding the implications of selecting appropriate statistical methods, and computing appropriate descriptive statistics. The only competency extensively covered in required coursework for all master’s-level programs was understanding the implications of selecting appropriate statistical methods. The least covered competencies included understanding the purpose of meta-analysis and the uses of early stopping rules in clinical trials. Competencies considered to be less fundamental and more specialized tended to be covered less frequently in graduate courses.
While graduate courses in CTS tend to cover many statistical fundamentals, learning gaps exist, particularly for more specialized competencies. Educational material to fill these gaps is necessary for learners pursuing these activities.
It is increasingly essential for medical researchers to be literate in statistics, but the requisite degree of literacy is not the same for every statistical competency in translational research. Statistical competency can range from ‘fundamental’ (necessary for all) to ‘specialized’ (necessary for only some). In this study, we determine the degree to which each competency is fundamental or specialized.
We surveyed members of 4 professional organizations, targeting doctorally trained biostatisticians and epidemiologists who taught statistics to medical research learners in the past 5 years. Respondents rated 24 educational competencies on a 5-point Likert scale anchored by ‘fundamental’ and ‘specialized.’
There were 112 responses. Nineteen of 24 competencies were fundamental. The competencies considered most fundamental were assessing sources of bias and variation (95%), recognizing one’s own limits with regard to statistics (93%), identifying the strengths, and limitations of study designs (93%). The least endorsed items were meta-analysis (34%) and stopping rules (18%).
We have identified the statistical competencies needed by all medical researchers. These competencies should be considered when designing statistical curricula for medical researchers and should inform which topics are taught in graduate programs and evidence-based medicine courses where learners need to read and understand the medical research literature.