A clinical and translational scientist (CTS) often seeks to increase their knowledge of statistical topics to effectively conduct biomedical research studies. A common method for obtaining this knowledge is through existing online educational materials that are suggested by a biostatistical collaborator or identified by the CTS. However, the volume of available educational materials on diverse statistical topics makes the task of identifying high-quality educational resources at an appropriate level challenging and time consuming for CTSs and collaborative biostatisticians. In response to these challenges, the Biostats4You website was created, where existing online educational materials for a variety of statistical topics are vetted to identify those most appropriate for CTSs. In this manuscript, we describe the resource review process, provide information about statistical topics and resources currently available, and make recommendations for how CTSs and collaborative biostatisticians can utilize the Biostats4You website to improve training, mentoring, and collaborative research practices.

OBJECTIVES/GOALS: The overall goals of this project are: (1) to demonstrate the utility of staff with cross-disciplinary skills in biostatistics and epidemiology as program managers for CTSA BERD Cores, and (2) examine streamlined processes in project triaging, consultations, and data extractions for CTSA service requests when individuals are in this role. METHODS/STUDY POPULATION: Biostatistics and epidemiology are partnered disciplines incorporated in a variety of research areas, especially in human health sciences and health care. For interdisciplinary teams, including individuals trained in both specialties results in efficient research collaborations. When these individuals are program managers for research navigation, processes become more effective and expedited for project timelines and workflows across the CTSA. Examples of this integration are described in vignettes from project triaging, statistical consultations, and data extractions from CTSA service requests. Process comparisons of previous and current workflows are presented to show the advantages of utilizing this type of program manager within these areas of BERD. RESULTS/ANTICIPATED RESULTS: This project defines three areas of integration for a program manager with cross-disciplinary training in biostatistics and epidemiology: (1) project triaging to statistical teams, (2) study design and analysis consultations, and (3) data extractions. Each of these areas demonstrates prior processes that once would require multiple steps in CTSA service requests (including time, resources, and personnel) and identified common issues (slow response to requests, data re-extractions, and challenges providing statistical support) that are now avoided with a manager with cross-training in biostatistics and epidemiology methods. The advantages and disadvantages of integrating these individuals are also described. DISCUSSION/SIGNIFICANCE: BERD Cores seeking improvements to research navigation processes can gain efficiency by incorporating program managers with cross-disciplinary training in biostatistics and epidemiology. BERD Cores may also use this as a case study for translational science with innovation to longstanding challenges in CTSA research workflows.

Research articles in the clinical and translational science literature commonly use quantitative data to inform evaluation of interventions, learn about the etiology of disease, or develop methods for diagnostic testing or risk prediction of future events. The peer review process must evaluate the methodology used therein, including use of quantitative statistical methods. In this manuscript, we provide guidance for peer reviewers tasked with assessing quantitative methodology, intended to complement guidelines and recommendations that exist for manuscript authors. We describe components of clinical and translational science research manuscripts that require assessment including study design and hypothesis evaluation, sampling and data acquisition, interventions (for studies that include an intervention), measurement of data, statistical analysis methods, presentation of the study results, and interpretation of the study results. For each component, we describe what reviewers should look for and assess; how reviewers should provide helpful comments for fixable errors or omissions; and how reviewers should communicate uncorrectable and irreparable errors. We then discuss the critical concepts of transparency and acceptance/revision guidelines when communicating with responsible journal editors.

Ongoing professional development is important for collaborative biostatisticians, as it enables them to remain current with the latest advances in statistical methodology and software, refine their analytical skills, and expand their domain knowledge, thereby facilitating their ability to contribute effectively to biomedical research. Although external opportunities for professional development, such as attending conferences and workshops, are widely recognized and valued in the field of biostatistics, there has been comparatively little attention given to internal opportunities for enhancing the skills and knowledge of biostatisticians which can be implemented with lower financial and time investment than external offerings. The purpose of this paper is to offer guidance for ongoing internal professional development activities that can be employed by collaborative biostatistics units in universities and academic medical centers to complement structured curricula and initial training. Specific examples of activities are provided so that collaborative biostatisticians and/or managers of biostatistical units can flexibly combine components to create an appropriately scaled, customized program that meets the needs of themselves or of the unit.

Background: A team of infectious diseases physicians, infectious diseases pharmacists, clinical laboratorians, and researchers collaborated to assess the management of lower respiratory tract infections (LRTIs). In 1 sample from our institution, 96.1% of pneumonia cases were prescribed antibiotics, compared to 85.0% in a comparison group. A collaborative effort led to the development of a protocol for procalcitonin (PCT)-guided antibiotic prescribing that was approved by several hospital committees, including the Antimicrobial Stewardship Committee and the Healthcare Pharmacy & Therapeutics Committee in December 2020. The aim of this analysis was to develop baseline information on PCT ordering and antibiotic prescribing patterns in LRTIs. Methods: We evaluated all adult inpatients (March–September 2019 and 2020) with a primary diagnosis of LRTI who received at least 1 antibiotic. Two cohorts were established to observe any potential differences in the 2 most recent years prior to adoption of the PCT protocol. Data (eg, demographics, specific diagnosis, length of stay, antimicrobial therapy and duration, PCT labs, etc) were obtained from the UK Center for Clinical and Translational Science, and the study was approved by the local IRB. The primary outcome of interest was antibiotic duration; secondary outcomes of interest were PCT orders, discharge antibiotic prescription, and inpatient length of stay. Results: In total, 432 patients (277 in 2019 and 155 in 2020) were included in this analysis. The average patient age was 61.2 years (SD, ±13.7); 47.7% were female; and 86.1% were white. Most patients were primarily diagnosed with pneumonia (58.8%), followed by COPD with complication (40.5%). In-hospital mortality was 3.5%. The minority of patients had any orders for PCT (29.2%); among them, most had only 1 PCT level measured (84.1%). The median length of hospital stay was 4 days (IQR, 2–6), and the median duration of antibiotic therapy was 4 days (IQR, 3–6). Conclusions: The utilization of PCT in LRTIs occurs in the minority of patient cases at our institution and mostly as a single measurement. The development and implementation of a PCT-guided therapy could help optimize antibiotic usage in patients with LRTIs.

Funding: No

Disclosures: None

The availability of large healthcare datasets offers the opportunity for researchers to navigate the traditional clinical and translational science research stages in a nonlinear manner. In particular, data scientists can harness the power of large healthcare datasets to bridge from preclinical discoveries (T0) directly to assessing population-level health impact (T4). A successful bridge from T0 to T4 does not bypass the other stages entirely; rather, effective team science makes a direct progression from T0 to T4 impactful by incorporating the perspectives of researchers from every stage of the clinical and translational science research spectrum. In this exemplar, we demonstrate how effective team science overcame challenges and, ultimately, ensured success when a diverse team of researchers worked together, using healthcare big data to test population-level substance use disorder (SUD) hypotheses generated from preclinical rodent studies. This project, called Advancing Substance use disorder Knowledge using Big Data (ASK Big Data), highlights the critical roles that data science expertise and effective team science play in quickly translating preclinical research into public health impact.