Biostatisticians increasingly use large language models (LLMs) to enhance efficiency, yet practical guidance on responsible integration is limited. This study explores current LLM usage, challenges, and training needs to support biostatisticians.

A cross-sectional survey was conducted across three biostatistics units at two academic medical centers. The survey assessed LLM usage across three key professional activities: communication and leadership, clinical and domain knowledge, and quantitative expertise. Responses were analyzed using descriptive statistics, while free-text responses underwent thematic analysis.

Of 208 eligible biostatisticians (162 staff and 46 faculty), 69 (33.2%) responded. Among them, 44 (63.8%) reported using LLMs; of the 43 who answered the frequency question, 20 (46.5%) used them daily and 16 (37.2%) weekly. LLMs improved productivity in coding, writing, and literature review; however, 29 of 41 respondents (70.7%) reported significant errors, including incorrect code, statistical misinterpretations, and hallucinated functions. Key verification strategies included expertise, external validation, debugging, and manual inspection. Among 58 respondents providing training feedback, 44 (75.9%) requested case studies, 40 (69.0%) sought interactive tutorials, and 37 (63.8%) desired structured training.

LLM usage is notable among respondents at two academic medical centers, though response patterns likely reflect early adopters. While LLMs enhance productivity, challenges like errors and reliability concerns highlight the need for verification strategies and systematic validation. The strong interest in training underscores the need for structured guidance. As an initial step, we propose eight core principles for responsible LLM integration, offering a preliminary framework for structured usage, validation, and ethical considerations.

Children continue to be an underrepresented population in research and clinical trials due to difficulties encountered in recruitment, assenting, and retention processes. “Sofia Learns About Research” is a children’s activity book that introduces youth to clinical research and basic elements of clinical trials.

Development of the activity book began in 2016, with publication of the first paper version in 2017 and an online version adapted for computer and tablet users in 2019. In 2019, we developed internal review board-approved pre/post surveys with five statements (written at ≤ 3rd-grade level) reflecting key concepts covered in the book. Participants were asked to indicate whether they agreed, disagreed, or were not sure about each of the statements and if they would ever want to be part of a research study. Preliminary analyses included descriptive statistics and cross-tabulations with chi squares.

Despite delays in dissemination and outreach due to the COVID-19 pandemic, we obtained feedback from over 170 diverse persons across a spectrum of communities and community partners. After book exposure, more participants knew that both children and parents have to assent/consent and that participants can withdraw from a study at any time.

The book is an important advocacy tool with a long-term aim of increasing children’s knowledge and awareness about clinical research, ultimately leading to enhanced participation in clinical research and trials.

Clinical research is critical for healthcare advancement, but participant recruitment remains challenging. Clinical research professionals (CRPs; e.g., clinical research coordinator, research assistant) perform eligibility prescreening, ensuring adherence to study criteria while upholding scientific and ethical standards. This study investigates the key information CRP prioritizes during eligibility prescreening, providing insights to optimize data standardization, and recruitment approaches.

We conducted a freelisting survey targeting 150 CRPs from diverse domains (i.e., neurological disorders, rare diseases, and other diseases) where they listed essential information they look for from medical records, participant/caregiver inquiries, and discussions with principal investigators to determine a potential participant’s research eligibility. We calculated the salience scores of listed items using Anthropac, followed by a two-level analytic procedure to classify and thematically categorize the data.

The majority of participants were female (81%), identified as White (44%) and as non-Hispanic (64.5%). The first-level analysis universally emphasized age, medication list, and medical history across all domains. The second-level analysis illuminated domain-specific approaches in information retrieval: for instance, history of present illness was notably significant in neurological disorders during participant and principal investigator inquiries, while research participation was distinctly salient in potential participant inquiries within the rare disease domain.

This study unveils the intricacies of eligibility prescreening, with both universal and domain-specific methods observed. Variations in data use across domains suggest the need for tailored prescreening in clinical research. Incorporating these insights into CRP training and refining prescreening tools, combined with an ethical, participant-focused approach, can advance eligibility prescreening practices.

Community inclusion in research may increase the quality and relevance of research, but doing so in an equitable way is complex. Novel approaches used to build engagement with historically marginalized communities in other sectors may have relevance in the clinical research sector.

To address long-standing gaps and challenges, a stakeholder group was convened to develop a theory of change (ToC), a structured method for obtaining input from stakeholders to enhance the design, conduct, and dissemination of research. The stakeholder group, comprised of Black residents within a metropolitan area, followed a structured monthly meeting schedule for 12 months to produce an outcome map, a model that formally defines aspects of research and engagement for this community.

Stakeholders reported significant improvements in trust in and engagement with research over the 12-month period, but not changes in health empowerment (individual, organizational, or community level). Through this convening process, a ToC and outcome map were developed with the focus of building bidirectional relationships between groups identifying as Black, Indigenous, and People of Color (BIPOC) and researchers in Boston, MA. Additionally, the group developed a community ownership model and guidelines for researchers to adhere to when utilizing the ToC and outcome map with BIPOC communities.

Co-ownership of models to develop bidirectional relationships between researchers and community members, such as the ToC and outcome map, may advance and further the value and reach of community-based participatory research while increasing levels of trust and engagement in research.

The value of Source Data Verification (SDV) has been a common theme in the applied Clinical Translational Science literature. Yet, few published assessments of SDV quality exist even though they are needed to design risk-based and reduced monitoring schemes. This review was conducted to identify reports of SDV quality, with a specific focus on accuracy.

A scoping review was conducted of the SDV and clinical trial monitoring literature to identify articles addressing SDV quality. Articles were systematically screened and summarized in terms of research design, SDV context, and reported measures.

The review found significant heterogeneity in underlying SDV methods, domains of SDV quality measured, the outcomes assessed, and the levels at which they were reported. This variability precluded comparison or pooling of results across the articles. No absolute measures of SDV accuracy were identified.

A definitive and comprehensive characterization of SDV process accuracy was not found. Reducing the SDV without understanding the risk of critical findings going undetected, i.e., SDV sensitivity, is counter to recommendations in Good Clinical Practice and the principles of Quality by Design. Reference estimates (or methods to obtain estimates) of SDV accuracy are needed to confidently design risk-based, reduced SDV processes for clinical studies.

Research study complexity refers to variables that contribute to the difficulty of a clinical trial or study. This includes variables such as intervention type, design, sample, and data management. High complexity often requires more resources, advanced planning, and specialized expertise to execute studies effectively. However, there are limited instruments that scale study complexity across research designs. The purpose of this study was to develop and establish initial psychometric properties of an instrument that scales research study complexity.

Technical and grammatical principles were followed to produce clear, concise items using language familiar to researchers. Items underwent face, content, and cognitive validity testing through quantitative surveys and qualitative interviews. Content validity indices were calculated, and iterative scale revision was performed. The instrument underwent pilot testing using 2 exemplar protocols, asking participants (n = 31) to score 25 items (e.g., study arms, data collection procedures).

The instrument (Research Complexity Index) demonstrated face, content, and cognitive validity. Item mean and standard deviation ranged from 1.0 to 2.75 (Protocol 1) and 1.31 to 2.86 (Protocol 2). Corrected item-total correlations ranged from .030 to .618. Eight elements appear to be under correlated to other elements. Cronbach’s alpha was 0.586 (Protocol 1) and 0.764 (Protocol 2). Inter-rater reliability was fair (kappa = 0.338).

Initial pilot testing demonstrates face, content, and cognitive validity, moderate internal consistency reliability and fair inter-rater reliability. Further refinement of the instrument may increase reliability thus providing a comprehensive method to assess study complexity and related resource quantification (e.g., staffing requirements).

A gap in the literature exists pertaining to a global research nurse/research midwife resources and communication skill set necessary to engage with participants of diverse populations and geographic regions in the community or home-based conduct of decentralized clinical trials.

An embedded mixed methods study was conducted to examine research nurse/research midwife knowledge base, experiences, and communication skill sets pertaining to decentralized trials across global regions engaged in remote research: the USA, Republic of Ireland, United Kingdom, and Australia.

An online survey was deployed across international research nurse/research midwife stakeholder groups, collecting demographics, decentralized trial experience, barriers and facilitators to optimal trial conduct, and the self-perceived communication competence (SPCC) and interpersonal communication competence (IPCC) instruments.

86 research nurses and research midwives completed the survey across all countries: The SPCC and IPCC results indicated increased clinical research experience significantly correlated with increased SPCC score (p < 0.05). Qualitative content analysis revealed five themes: (1) Implications for Role, (2) Safety and Wellbeing, (3) Training and Education, (4) Implications for Participants, and (5) Barriers and Facilitators.

Common trends and observations across the global sample can inform decentralized trial resource allocation and policy pertaining to the research nurse/research midwife workforce. This study demonstrates shared cultural norms of research nursing and midwifery across varied regional clinical trial ecosystems.

To compare how clinical researchers generate data-driven hypotheses with a visual interactive analytic tool (VIADS, a visual interactive analysis tool for filtering and summarizing large datasets coded with hierarchical terminologies) or other tools.

We recruited clinical researchers and separated them into “experienced” and “inexperienced” groups. Participants were randomly assigned to a VIADS or control group within the groups. Each participant conducted a remote 2-hour study session for hypothesis generation with the same study facilitator on the same datasets by following a think-aloud protocol. Screen activities and audio were recorded, transcribed, coded, and analyzed. Hypotheses were evaluated by seven experts on their validity, significance, and feasibility. We conducted multilevel random effect modeling for statistical tests.

Eighteen participants generated 227 hypotheses, of which 147 (65%) were valid. The VIADS and control groups generated a similar number of hypotheses. The VIADS group took a significantly shorter time to generate one hypothesis (e.g., among inexperienced clinical researchers, 258 s versus 379 s, p = 0.046, power = 0.437, ICC = 0.15). The VIADS group received significantly lower ratings than the control group on feasibility and the combination rating of validity, significance, and feasibility.

The role of VIADS in hypothesis generation seems inconclusive. The VIADS group took a significantly shorter time to generate each hypothesis. However, the combined validity, significance, and feasibility ratings of their hypotheses were significantly lower. Further characterization of hypotheses, including specifics on how they might be improved, could guide future tool development.