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Collaborative quantitative scientists, including biostatisticians, epidemiologists, bio-informaticists, and data-related professionals, play vital roles in research, from study design to data analysis and dissemination. It is imperative that academic health care centers (AHCs) establish an environment that provides opportunities for the quantitative scientists who are hired as staff to develop and advance their careers. With the rapid growth of clinical and translational research, AHCs are charged with establishing organizational methods, training tools, best practices, and guidelines to accelerate and support hiring, training, and retaining this staff workforce. This paper describes three essential elements for building and maintaining a successful unit of collaborative staff quantitative scientists in academic health care centers: (1) organizational infrastructure and management, (2) recruitment, and (3) career development and retention. Specific strategies are provided as examples of how AHCs can excel in these areas.

We describe an innovative, semester-long course in biology and communication skills for master's degree students in biostatistics. The primary goal of the course is to make the connection between biological science and statistics more explicit. The secondary goals are to teach oral and written communication skills in an appropriate context for applied biostatisticians, and to teach a structured approach to thinking that enables students to become lifelong learners in biology, study design, and the application of statistics to biomedical research. Critical evaluation of medical literature is the method used to teach biology and communication. Exercises are constructivist in nature, designed to be hands-on and encourage reflection through writing and oral communication. A single disease area (cancer) provides a motivating example to: (1) introduce students to the most commonly used study designs in medical and public health research, (2) illustrate how study design is used to address questions about human biology and disease, (3) teach basic biological concepts necessary for a successful career in biostatistics, and (4) train students to read and critically evaluate publications in peer-reviewed journals. We describe the design and features of the course, the intended audience, and provide detailed examples for instructors interested in designing similar courses.

Student evaluation is a key consideration for educational program administrators because program success depends on students' ability to demonstrate successful development of core competencies. Student evaluations must therefore be aligned with learning objectives and overall program goals. Graduate level educational programs typically incorporate course-level and program-level evaluations, e.g., a final examination in a single course vs. a qualifying examination that assesses knowledge gained from several courses. While there is often considerable attention given to the structure of these evaluations at the program level, the format is typically left to the instructor's discretion. We argue in this article that there are administrative advantages to encouraging instructors to adopt creative forms of assessment that extend beyond the typical concerns related to program structure. Specifically, we argue that advantages can be gained in terms of increasing student engagement, adding real world context to student evaluations, maintaining positive program culture, and reducing the opportunity for cheating. We present two examples of creative assessments implemented in a 2-year Master of Biostatistics program, along with a discussion of three key questions administrators should consider as they work with instructors to develop innovative assessment methods: (1) what changes to make; (2) in what order to make those changes; and (3) how to consult with instructors about making those changes.

We describe an innovative preorientation curriculum (POC) for a Master of Biostatistics (MB) program. The goal of the POC is to fill critical skills gaps for students entering the MB program from heterogeneous backgrounds so they are prepared to engage in the program's rigorous, fast-paced training upon arrival. To achieve this goal, we introduce a structured approach to thinking that forms the foundation of a sound mental map of Biostatistics, which will assist students in their subsequent efforts to master the discipline. Based on constructivist principles, the POC covers mathematical and statistical theory, data analysis methods, programming, and statistical practice through a sequence of instruction that encourages reflection, extension, and connection between topics. The POC is modular, self-paced, and offered online via a cloud-hosted interactive learning management system (LMS). Students are required to complete the curriculum prior to the MB program orientation. We describe the rationale, design, features, and initial evaluation of the POC. Finally, to help programs interested in designing similar curricula, we provide a detailed instruction sequence description of one topic.

IntroductionStatistical 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.MethodsWe 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.ResultsThere 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.ConclusionWhile 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.

The ability to bridge multiple disciplines is critical to the successful practice of collaborative statistics, yet the literature on statistical education devotes relatively little attention to how this skill can be taught. Our goal here is to describe a general conceptual framework within which a curriculum on communication and leadership could ultimately be organized. The primary research question pertains to whether an actionable model of cross-disciplinary communication for collaborative statisticians can be developed, and our task here is to describe such a model and also to illustrate its use. Within this model most communications either share or request information. For example, statisticians might provide information about statistics (e.g., specific statistical approaches, general statistical principles), comment on the clinician's understanding of statistics, share their understanding of clinical content, and request information (e.g., about clinical content, the design and execution of the study being discussed, etc.). Clinical investigators contribute an analogous set of components. In addition, a critical element to the interaction is the higher-level task of developing a mutually understood agreement about the work to be performed: in essence, proposing and negotiating such an agreement. The model is illustrated using a case study, and general qualitative feedback from investigators who performed the case study was obtained, commenting on both successful and unsuccessful interactions with statisticians. Implications for curriculum development are discussed.

IntroductionStatistical 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.MethodsWe 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.ResultsThere 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.ConclusionWhile 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.

PURPOSE:We aimed to study the associations between androgen-deprivation therapy (ADT)-induced weight changes and prostate cancer (PC) progression and mortality in men who had undergone radical prostatectomy (RP). METHODS:Data from the Shared Equal Access Regional Cancer Hospital (SEARCH) cohort were used to study the associations between weight change approximately 1-year post-ADT initiation and metastases, castration-resistant prostate cancer (CRPC), all-cause mortality (ACM), and PC-specific mortality (PCSM) in 357 patients who had undergone RP between 1988 and 2014. We estimated hazard ratios (HR) and 95% confidence intervals (95% CI) using covariate-adjusted Cox regression models for associations between weight loss, and weight gains of 2.3 kg or more, and PC progression and mortality post-ADT. RESULTS:During a median (IQR) follow-up of 81 (46-119) months, 55 men were diagnosed with metastases, 61 with CRPC, 36 died of PC, and 122 died of any cause. In multivariable analysis, weight loss was associated with increases in risks of metastases (HR 3.13; 95% CI 1.40-6.97), PCSM (HR 4.73; 95% CI 1.59-14.0), and ACM (HR 2.16; 95% CI 1.25-3.74) compared with mild weight gains of ≤ 2.2. Results were slightly attenuated but remained statistically significant in analyses that accounted for competing risks of non-PC death. Estimates for the associations between weight gains of ≥ 2.3 kg and metastases (HR 1.58; 95% CI 0.73-3.42), CRPC (HR 1.33; 95% CI 0.66-2.66), and PCSM (HR 2.44; 95% CI 0.84-7.11) were elevated, but not statistically significant. CONCLUSIONS:Our results suggest that weight loss following ADT initiation in men who have undergone RP is a poor prognostic sign. If confirmed in future studies, testing ways to mitigate weight loss post-ADT may be warranted.

Data science consulting and collaboration units (DSUs) are core infrastructure for research at universities. Activities span data management, study design, data analysis, data visualization, predictive modelling, preparing reports, manuscript writing and advising on statistical methods and may include an experiential or teaching component. Partnerships are needed for a thriving DSU as an active part of the larger university network. Guidance for identifying, developing and managing successful partnerships for DSUs can be summarized in six rules: (1) align with institutional strategic plans, (2) cultivate partnerships that fit your mission, (3) ensure sustainability and prepare for growth, (4) define clear expectations in a partnership agreement, (5) communicate and (6) expect the unexpected. While these rules are not exhaustive, they are derived from experiences in a diverse set of DSUs, which vary by administrative home, mission, staffing and funding model. As examples in this paper illustrate, these rules can be adapted to different organizational models for DSUs. Clear expectations in partnership agreements are essential for high quality and consistent collaborations and address core activities, duration, staffing, cost and evaluation. A DSU is an organizational asset that should involve thoughtful investment if the institution is to gain real value. [ABSTRACT FROM AUTHOR]

With increased access to biomedical and electronic health records data and the complexity of research questions, individuals in residency programmes who aim to conduct research require specialized educational programmes and biostatistics support. Biostatistics collaboration units in academic health centres often work with residents to conduct data‐intensive research. These units face numerous challenges related to providing training in statistical literacy and collaborating on resident‐led research within very restricted timelines. Since 2019, the Duke Biostatistics, Epidemiology, and Research Design (BERD) Methods Core has supported over 247 resident‐led projects by developing tools and resources to address these challenges. This manuscript presents novel processes and training materials that other institutions can use to help biostatistics collaboration units effectively support resident training programmes. We provide a framework to support the development of collaborative teams, along with specialized training materials for residents who collaborate with these teams. [ABSTRACT FROM AUTHOR]

The emphasis on team science in clinical and translational research increases the importance of collaborative biostatisticians (CBs) in healthcare. Adequate training and development of CBs ensure appropriate conduct of robust and meaningful research and, therefore, should be considered as a high-priority focus for biostatistics groups. Comprehensive training enhances clinical and translational research by facilitating more productive and efficient collaborations. While many graduate programs in Biostatistics and Epidemiology include training in research collaboration, it is often limited in scope and duration. Therefore, additional training is often required once a CB is hired into a full-time position. This article presents a comprehensive CB training strategy that can be adapted to any collaborative biostatistics group. This strategy follows a roadmap of the biostatistics collaboration process, which is also presented. A TIE approach (Teach the necessary skills, monitor the Implementation of these skills, and Evaluate the proficiency of these skills) was developed to support the adoption of key principles. The training strategy also incorporates a "train the trainer" approach to enable CBs who have successfully completed training to train new staff or faculty. [ABSTRACT FROM AUTHOR]