Evaluation of benefits beyond quantitative academic outputs is essential in determining translational research value. We used the Translational Science Benefits Model (TSBM) to examine the impact of the QUARTET USA trial using 30 benefits across 4 domains: Clinical, Community, Economic, and Policy. We found that the QUARTET USA trial demonstrated impact in six areas within the Clinical, and Community domains and had potential impact in two additional areas within the Community and Economic domains. Use of the TSBM supports the value of the QUARTET USA trial, which can be used as a template for future cardiovascular trials.

Research studies involving human subjects require collection of and reporting on demographic data related to race and ethnicity. However, existing practices lack standardized guidelines, leading to misrepresentation and biased inferences and conclusions for underrepresented populations in research studies. For instance, sometimes there is a misconception that self-reported racial or ethnic identity may be treated as a biological variable with underlying genetic implications, overlooking its role as a social construct reflecting lived experiences of specific populations. In this manuscript, we use the We All Count data equity framework, which organizes data projects across seven stages: Funding, Motivation, Project Design, Data Collection, Analysis, Reporting, and Communication. Focusing on data collection and analysis, we use examples – both real and hypothetical – to review common practice and provide critiques and alternative recommendations. Through these examples and recommendations, we hope to provide the reader with some ideas and a starting point as they consider embedding a lens of justice, equity, diversity, and inclusivity from research conception to dissemination of findings.

Interim analyses in clinical trials can take on a multitude of forms. They are often used to guide Data and Safety Monitoring Board (DSMB) recommendations to study teams regarding recruitment targets for large, later-phase clinical trials. As collaborative biostatisticians working and teaching in multiple fields of research and across a broad array of trial phases, we note the large heterogeneity and confusion surrounding interim analyses in clinical trials. Thus, in this paper, we aim to provide a general overview and guidance on interim analyses for a nonstatistical audience. We explain each of the following types of interim analyses: efficacy, futility, safety, and sample size re-estimation, and we provide the reader with reasoning, examples, and implications for each. We emphasize that while the types of interim analyses employed may differ depending on the nature of the study, we would always recommend prespecification of the interim analytic plan to the extent possible with risk mitigation and trial integrity remaining a priority. Finally, we posit that interim analyses should be used as tools to help the DSMB make informed decisions in the context of the overarching study. They should generally not be deemed binding, and they should not be reviewed in isolation.

Prophylaxis against spontaneous bacterial peritonitis (SBP) is recommended for select patients with cirrhosis, but long-term antibiotic therapy has risks. We evaluated concordance with guideline recommendations in 179 veterans with cirrhosis; 55% received guideline-concordant management of SBP prophylaxis. Despite stable guideline recommendations since 2012, guideline adherence remains low.

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.