Best Practices for Biostatistical Consultation and Collaboration in Academic Health Centers

Advanced Research Institute (ARI): Supporting the Geriatric Mental Health Research Pipeline

The Advanced Research Institute (ARI) in Mental Health and Aging is a NIMH-funded mentoring network to help transition early-career faculty to independent investigators and scientific leaders. Since 2004, ARI has enrolled 184 Scholars from 61 institutions across 34 states. We describe the ARI components and assess the impact and outcomes of ARI on research careers of participants. Outcomes of ARI graduates (n = 165) came from NIH Reporter, brief surveys, and CVs: 87.3% remained active researchers, 83.6% performed scientific service, and 80.6% obtained federal grants. A population-based analysis examined NIMH mentored K awardees initially funded from 2002-2018 (n = 1160): in this group, 77.1% (47/61) of ARI participants versus 49.5% (544/1099) of nonparticipants obtained an R01. Controlling for time, ARI participants were 3.2 times more likely to achieve R01 funding than nonparticipants. Given the struggle to reduce attrition from the research career pipeline, the effectiveness of ARI model could be relevant to other fields.

Building a strong collaborative biostatistics workforce: Strategies for effective intra-unit professional development activities

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.

"Technical" Contributors and Authorship Distribution in Health Science

In health sciences, technical contributions may be undervalued and excluded in the author byline. In this paper, I demonstrate how authorship is a historical construct which perpetuates systemic injustices including technical undervaluation. I make use of Pierre Bourdieu's conceptual work to demonstrate how the power dynamics at play in academia make it very challenging to change the habitual state or "habitus". To counter this, I argue that we must reconceive technical contributions to not be a priori less important based on its nature when assigning roles and opportunities leading to authorship. I make this argument based on two premises. First, science has evolved due to major information and biotechnological innovation; this requires 'technicians' to acquire and exercise a commensurate high degree of both technical and intellectual expertise which in turn increases the value of their contribution. I will illustrate this by providing a brief historical view of work statisticians, computer programmers/data scientists and laboratory technicians. Second, excluding or undervaluing this type of work is contrary to norms of responsibility, fairness and trustworthiness of the individual researchers and of teams in science. Although such norms are continuously tested because of power dynamics, their importance is central to ethical authorship practice and research integrity. While it may be argued that detailed disclosure of contributions (known as contributorship) increases accountability by clearly identifying who did what in the publication, I contend that this may unintentionally legitimize undervaluation of technical roles and may decrease integrity of science. Finally, this paper offers recommendations to promote ethical inclusion of technical contributors.

Collaborative biostatistics and epidemiology in academic medical centres: A survey to assess relationships with health researchers and ethical implications

The role of collaborative biostatisticians and epidemiologists in academic medical centers and how their degree type, supervisor type, and sex influences recognition and feelings of respect is poorly understood. We conducted a cross-sectional survey of self-identified biostatisticians and epidemiologists working in academic medical centers in the US or Canada. The survey was sent to 341 contacts at 125 institutions who were asked to forward the survey invitation to faculty and staff at their institution and posted on Community sections of the American Statistical Association website. Participants were asked a variety of questions including if they felt pressured to produce specific results, whether they had intellectual and ethical freedom to pursue appropriate use of statistical methods in collaborative research, and if they felt their contributions were appropriately recognized by collaborators. We received responses from 314 biostatisticians or related methodologists. A majority were female (59%), had a doctorate degree (52%), and reported a statistician or biostatistician supervisor (69%). Overall, most participants felt valued by their collaborators, but that they did not have sufficient calendar time to meet deadlines. Doctoral-level participants reported more autonomy in their collaborations than master's level participants. Females were less likely to feel recognized and respected compared to males. The survey results suggest that while most respondents felt valued by their collaborators, they have too many projects and need more time to critically review research. Further research is needed to understand why response differs by sex and how these responses fluctuate over time.

A comprehensive survey of collaborative biostatistics units in academic health centers

The organizational structures of collaborative biostatistics units in academic health centers (AHCs) in the United States and their important contributions to research are an evolving and active area of discussion and inquiry. Collaborative biostatistics units may serve as a centralized resource to investigators across various disciplines or as shared infrastructure for investigators within a discipline (e.g., cancer), or a combination of both. The characteristics of such units vary greatly, and there has been no comprehensive review of their organizational structures described in the literature to date. This manuscript summarizes the current infrastructure of such units using responses from 129 leaders. Most leaders were over 45 years old, held doctoral degrees, and were on a 12-month appointment. Over half were tenured or on a tenure track and held primary appointments in a school of medicine. Career advancement metrics most important included being funded as co-investigator on NIH grants and being either first or second author on peer-reviewed publications. Team composition was diverse in terms of expertise and training, and funding sources were typically hybrid. These results provide a benchmark for collaboration models and evaluation and may be used by institutional administrators as they build, evaluate, or restructure current collaborative quantitative support infrastructure.

Establishing a Data Science Unit in an Academic Medical Center: An Illustrative Model

The field of data science has great potential to address critical questions relevant for academic medical centers. Data science initiatives are consequently being established within academic medicine. At the cornerstone of such initiatives are scientists who practice data science. These scientists include biostatisticians, clinical informaticians, database and software developers, computational scientists, and computational biologists. Too often, however, those involved in the practice of data science are viewed by academic leadership as providing a noncomplex service to facilitate research and further the careers of other academic faculty. The authors contend that the success of data science initiatives relies heavily on the understanding that the practice of data science is a critical intellectual contribution to the overall science conducted at an academic medical center. Further, careful thought by academic leadership is needed for allocation of resources devoted to the practice of data science. At the Stanford University School of Medicine, the authors have developed an innovative model for a data science collaboratory based on 4 fundamental elements: an emphasis on collaboration over consultation, a subscription-based funding mechanism that reflects commitment by key partners, an investment in the career development of faculty who practice data science, and a strong educational component for data science members in team science and for clinical and translational investigators in data science. As data science becomes increasingly essential to learning health systems, centers that specialize in the practice of data science are a critical component of the research infrastructure and intellectual environment of academic medical centers.

Developing an Institute for Health Care Delivery Science: successes, challenges, and solutions in the first five years

Medical knowledge is increasing at an exponential rate. At the same time, unexplained variations in practice and patient outcomes and unacceptable rates of medical errors and inefficiencies in health care delivery have emerged. Our Institute for Health Care Delivery Science (I-HDS) began in 2014 as a novel platform to conduct multidisciplinary healthcare delivery research. We followed ten strategies to develop a successful institute with excellence in methodology and strong understanding of the value of team science. Our work was organized around five hubs: 1) Quality/Process Improvement and Systematic Review, 2) Comparative Effectiveness Research, Pragmatic Clinical Trials, and Predictive Analytics, 3) Health Economics and Decision Modeling, 4) Qualitative, Survey, and Mixed Methods, and 5) Training and Mentoring. In the first 5 years of the I-HDS, we have identified opportunities for change in clinical practice through research using our health system's electronic health record (EHR) data, and designed programs to educate clinicians in the value of research to improve patient care and recognize efficiencies in processes. Testing the value of several model interventions has guided prioritization of evidence-based quality improvements. Some of the changes in practice have already been embedded in the EHR workflow successfully. Development and sustainability of the I-HDS has been fostered by a mix of internal and external funding, including philanthropic foundations. Challenges remain due to the highly competitive funding environment and changes needed to adapt the EHR to healthcare delivery research. Further stakeholder engagement and culture change working with hospital leadership and I-HDS core and affiliate members continues.

Incorporating professional recommendations into a graduate-level statistical consulting laboratory: A case study

Introduction:

There has been a recent trend in medical research towards a more collaborative relationship between statisticians and clinical investigators. This has led to an increased focus on the most efficient and effective ways to structure, conduct, and measure the impact of organizations that provide statistical services to clinical investigators. Several recent guidelines and recommendations on the conduct of statistical consulting services(SCSs) have been made in response to this need, focusing on larger SCSs consisting primarily of faculty and staff statisticians. However, the application of these recommendations to consulting services primarily staffed by graduate students, which have the dual role of providing a professional service and training, remains unclear.

Methods:

Guidelines and recommendations, primarily from the Clinical and Translational Science (CTSA) consortium, were applied to a SCS staffed primarily by graduate students in an academic health center. A description of the organizational structure and outcomes after 3 years of operation is presented.

Results:

The guidelines recommended by the CTSA consortium and other groups were successfully incorporated into the graduate consulting laboratory. At almost one new project request per week, the consulting laboratory demonstrated a large bandwidth and had an excellent feedback from investigators.

Conclusions:

Guidelines developed for larger statistical consulting organizations are able to be applied in student-led consultation organizations. Outcomes and recommendations from 3.5 years of operation are used to describe the successes and challenges we have encountered.

Methods for training collaborative biostatisticians

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.

What makes a biostatistician?

Biostatisticians play an important role in medical research. They are co-responsible for an appropriate and efficient study design, they are involved in monitoring the study conduct, they plan and perform the data analysis, and they are involved in interpreting and publishing the results. However, how are the biostatisticians prepared for their tasks and responsibilities? Graduate programs in biostatistics are being offered, but some practicing biostatisticians completed their studies in a mathematical or epidemiological program, or obtained their degree in subject-specific fields (such as medicine or biology). Therefore, the expertise and the competencies can vary widely between the individual biostatisticians, also depending on the application field. In this article, focusing on European and US practices, we discuss the required professional expertise for the main areas of applications in the medical field as well as the necessary soft skill competencies of a biostatistician.

Ten Essential Practices for Developing or Reforming a Biostatistics Core for a NCI Designated Cancer Center

There are 69 National Cancer Institute (NCI) designated Cancer Centers (CCs) in the United States. Biostatistical collaboration is pivotal in cancer research, and support for a cancer biostatistics shared resource facility (C-BSRF) is included in the award. Although the services and staff needed in a C-BSRF have been outlined in general terms and best practices for biostatistical consultations and collaboration in an academic health center have been agreed upon, implementing these practices in the demanding setting of cancer centers interested in pursuing or maintaining NCI designation remains challenging. We surveyed all C-BSRF websites to assess their organizational charts, governance, size, services provided, and financial models and have identified 10 essential practices for the development of a successful C-BSRF. Here, we share our success with, and barriers to, implementation of these practices. Showcasing development plans for these essential practices resulted in an NCI score of "Excellent to Outstanding" for our C-BSRF in 2015, and performance metrics in 2016-2017 demonstrated notable improvement since our original Cancer Center Support Grant (CCSG) application in 2014. We believe that the essential practices described here can be adapted and adjusted, as needed, for CCs of various sizes and with different types of cancer research programs.

Statistical competencies for medical research learners: What is fundamental?

Introduction

It is increasingly essential for medical researchers to be literate in statistics, but the requisite degree of literacy is not the same for every statistical competency in translational research. Statistical competency can range from ‘fundamental’ (necessary for all) to ‘specialized’ (necessary for only some). In this study, we determine the degree to which each competency is fundamental or specialized.

Methods

We surveyed members of 4 professional organizations, targeting doctorally trained biostatisticians and epidemiologists who taught statistics to medical research learners in the past 5 years. Respondents rated 24 educational competencies on a 5-point Likert scale anchored by ‘fundamental’ and ‘specialized.’

Results

There were 112 responses. Nineteen of 24 competencies were fundamental. The competencies considered most fundamental were assessing sources of bias and variation (95%), recognizing one’s own limits with regard to statistics (93%), identifying the strengths, and limitations of study designs (93%). The least endorsed items were meta-analysis (34%) and stopping rules (18%).

Conclusion

We have identified the statistical competencies needed by all medical researchers. These competencies should be considered when designing statistical curricula for medical researchers and should inform which topics are taught in graduate programs and evidence-based medicine courses where learners need to read and understand the medical research literature.