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.

Behavioural animal experimentation is an inseparable part of research trying to understand the biological underpinnings of human behaviour, diseases and disorders. Working with animals comes with great responsibility to achieve reliable and reproducible results of highest scientific quality. In a simple step-by-step fashion, we highlight some common issues that may occur along the path to conducting behavioural animal experimentations and posit some solutions and grounds to ensure the excellence of work done in this research area while aspiring to improve conditions for laboratory animals. It entails topics of study design, animal and experimenter welfare, experimental considerations and frequentist biostatistics. At the end, we direct to some guidelines and manuals that may prove valuable to researchers in this field. Our ten simple tips and traps are meant for students who are learning about important concepts for the first time; graduates whose statistics training all too often has neglected the concept of power in experimental design; and researches who would like a light-hearted refresher on these topics. With this perspective, we hope that you will avoid falling into traps and find answers to what you always wanted to know about conducting behavioural animal experimentation.

The negative effects of excessive alcohol use include dependence, psychiatric co-morbidity and increased risk for suicide. A dominating risk factor is heritage. A large number of studies have addressed the genetic basis, either “candidate genes” in the brain reward system, or searched for unknown genes in family studies by linkage analysis. It is clear that no single gene polymorphism is of use in preventive medicine. A consistent finding, however, is that polymorphism in the alcohol dehydrogenase cluster and other metabolic pathways are of some relevance on a population basis, suggesting a link between alcohol toxicity in general and dependence. An emerging concern is potential gender differences as women, who are generally more sensitive, acquire male drinking habits.

While junior clinical researchers at academic medical institutions across the US often desire to be actively engaged in randomized-clinical trials, they often lack adequate resources and research capacity to design and implement them. This insufficiency hinders their ability to generate a rigorous randomization scheme to minimize selection bias and yield comparable groups. Moreover, there are limited online user-friendly randomization tools. Thus, we developed a free robust randomization app (RRApp). RRApp incorporates 6 major randomization techniques: simple randomization, stratified randomization, block randomization, permuted block randomization, stratified block randomization, and stratified permuted block randomization. The design phase has been completed, including robust server scripts and a straightforward user-interface using the “shiny” package in R. Randomization schemes generated in RRApp can be input directly into the Research Electronic Data Capture (REDCap) system. RRApp has been evaluated by biostatisticians and junior clinical faculty at the Icahn School of Medicine at Mount Sinai. Constructive feedback regarding the quality and functionality of RRApp was also provided by attendees of the 2016 Association for Clinical and Translational Statisticians Annual Meeting. RRApp aims to educate early stage clinical trialists about the importance of randomization, while simultaneously assisting them, in a user-friendly fashion, to generate reproducible randomization schemes.

As clinical researchers at academic medical institutions across the United States increasingly manage complex clinical databases and registries, they often lack the statistical expertise to utilize the data for research purposes. This statistical inadequacy prevents junior investigators from disseminating clinical findings in peer-reviewed journals and from obtaining research funding, thereby hindering their potential for promotion. Underrepresented minorities, in particular, confront unique challenges as clinical investigators stemming from a lack of methodologically rigorous research training in their graduate medical education. This creates a ripple effect for them with respect to acquiring full-time appointments, obtaining federal research grants, and promotion to leadership positions in academic medicine. To fill this major gap in the statistical training of junior faculty and fellows, the authors developed the Applied Statistical Independence in Biological Systems (ASIBS) Short Course. The overall goal of ASIBS is to provide formal applied statistical training, via a hybrid distance and in-person learning format, to junior faculty and fellows actively involved in research at US academic medical institutions, with a special emphasis on underrepresented minorities. The authors present an overview of the design and implementation of ASIBS, along with a short-term evaluation of its impact for the first cohort of ASIBS participants.

Data from psychiatric research frequently exhibit departures from Normality. Methods which utilise the data optimally to model the distribution directly are available. We highlight the issue of modelling skewness, resulting from screening instruments where the majority of respondents are healthy individuals and few participants have a value reflecting particular disorders.