Developing partnerships for academic data science consulting and collaboration units.

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.

Methods for building a staff workforce of quantitative scientists in academic health care.

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.

A guide to successful management of collaborative partnerships in quantitative research: An illustration of the science of team science.

Data-intensive research continues to expand with the goal of improving healthcare delivery, clinical decision-making, and patient outcomes. Quantitative scientists, such as biostatisticians, epidemiologists, and informaticists, are tasked with turning data into health knowledge. In academic health centres, quantitative scientists are critical to the missions of biomedical discovery and improvement of health. Many academic health centres have developed centralized Quantitative Science Units which foster dual goals of professional development of quantitative scientists and producing high quality, reproducible domain research. Such units then develop teams of quantitative scientists who can collaborate with researchers. However, existing literature does not provide guidance on how such teams are formed or how to manage and sustain them. Leaders of Quantitative Science Units across six institutions formed a working group to examine common practices and tools that can serve as best practices for Quantitative Science Units that wish to achieve these dual goals through building long-term partnerships with researchers. The results of this working group are presented to provide tools and guidance for Quantitative Science Units challenged with developing, managing, and evaluating Quantitative Science Teams. This guidance aims to help Quantitative Science Units effectively participate in and enhance the research that is conducted throughout the academic health centre-shaping their resources to fit evolving research needs.

Peer review of clinical and translational research manuscripts: Perspectives from statistical collaborators.

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.

Essential team science skills for biostatisticians on collaborative research teams.

Introduction: Despite the critical role that quantitative scientists play in biomedical research, graduate programs in quantitative fields often focus on technical and methodological skills, not on collaborative and leadership skills. In this study, we evaluate the importance of team science skills among collaborative biostatisticians for the purpose of identifying training opportunities to build a skilled workforce of quantitative team scientists. Methods: Our workgroup described 16 essential skills for collaborative biostatisticians. Collaborative biostatisticians were surveyed to assess the relative importance of these skills in their current work. The importance of each skill is summarized overall and compared across career stages, highest degrees earned, and job sectors. Results: Survey respondents were 343 collaborative biostatisticians spanning career stages (early: 24.2%, mid: 33.8%, late: 42.0%) and job sectors (academia: 69.4%, industry: 22.2%, government: 4.4%, self-employed: 4.1%). All 16 skills were rated as at least somewhat important by > 89.0% of respondents. Significant heterogeneity in importance by career stage and by highest degree earned was identified for several skills. Two skills ("regulatory requirements" and "databases, data sources, and data collection tools") were more likely to be rated as absolutely essential by those working in industry (36.5%, 65.8%, respectively) than by those in academia (19.6%, 51.3%, respectively). Three additional skills were identified as important by survey respondents, for a total of 19 collaborative skills. Conclusions: We identified 19 team science skills that are important to the work of collaborative biostatisticians, laying the groundwork for enhancing graduate programs and establishing effective on-the-job training initiatives to meet workforce needs.

Treatment Effect Estimates From Pilot Trials Are Unreliable.

CONTEXT: The CONSORT guideline defines a pilot trial as a small-scale version of a desired future efficacy trial that is intended to answer the key questions of whether and how a larger study should be done. For example, a pilot trial might evaluate different approaches to data collection or outcome measurement. However, pilot trials are unreliable for assessing treatment efficacy due to the statistical phenomenon called sampling variability. OBJECTIVES: In this tutorial we use computer simulation to demonstrate the influence of sampling variability on efficacy estimates from pilot trials, illustrating why pilot trial designs should not be used to evaluate whether a treatment is promising or not. METHODS: We simulate a 2-arm parallel group trial (N=20 per group) with a survival outcome as an example. Simulations are done under two scenarios: 1) the treatment is efficacious at the level of a hypothetical minimum clinically important difference (hazard ratio [HR] = 0.75); and 2) the treatment is not efficacious (HR=1). RESULTS: As expected, in both simulated scenarios the range of observed results is distributed around the true treatment effect, HR=0.75 or HR=1. Importantly, ∼20% of trials simulated under scenario 1 incorrectly suggest the treatment may be harmful (HR > 1). Under scenario 2, half of the simulated studies incorrectly suggest the treatment is beneficial. CONCLUSION: Treatment effect estimates from pilot trials should not be used to make future development decisions regarding a novel therapy because of the high risk of misleading conclusions.

Needles in a Haystack: Finding Qualitative and Quantitative Collaborators in Academic Medical Centers.

Translational research is a data-driven process that involves transforming scientific laboratory- and clinic-based discoveries into products and activities with real-world impact to improve individual and population health. Successful execution of translational research requires collaboration between clinical and translational science researchers, who have expertise in a wide variety of domains across the field of medicine, and qualitative and quantitative scientists, who have specialized methodologic expertise across diverse methodologic domains. While many institutions are working to build networks of these specialists, a formalized process is needed to help researchers navigate the network to find the best match and to track the navigation process to evaluate an institution's unmet collaborative needs. In 2018, a novel analytic resource navigation process was developed at Duke University to connect potential collaborators, leverage resources, and foster a community of researchers and scientists. This analytic resource navigation process can be readily adopted by other academic medical centers. The process relies on navigators with broad qualitative and quantitative methodologic knowledge, strong communication and leadership skills, and extensive collaborative experience. The essential elements of the analytic resource navigation process are as follows: (1) strong institutional knowledge of methodologic expertise and access to analytic resources, (2) deep understanding of research needs and methodologic expertise, (3) education of researchers on the role of qualitative and quantitative scientists in the research project, and (4) ongoing evaluation of the analytic resource navigation process to inform improvements. Navigators help researchers determine the type of expertise needed, search the institution to find potential collaborators with that expertise, and document the process to evaluate unmet needs. Although the navigation process can create a basis for an effective solution, some challenges remain, such as having resources to train navigators, comprehensively identifying all potential collaborators, and keeping updated information about resources as methodologists join and leave the institution.

A Visual Tool to Help Develop a Statistical Analysis Plan for Randomized Trials in Palliative Care.

Collaboration with a statistician about the design of a statistical analysis plan can be enhanced by illustrating how statisticians conceptualize their task. This conceptualization can be represented by a directed acyclic graph (DAG), which illustrates the statistician's approach and also provides an actionable tool to assist in the development of the plan.

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.

Experiential Learning Methods for Biostatistics Students: A Model for Embedding Student Interns in Academic Health Centers.

This manuscript describes an experiential learning program for future collaborative biostatisticians (CBs) developed within an academic medical center. The program is a collaborative effort between the Biostatistics, Epidemiology, and Research Design (BERD) Methods Core and the Master of Biostatistics (MB) program, both housed in the Department of Biostatistics and Bioinformatics at Duke University School of Medicine and supported in partnership with the Duke Clinical and Translational Science Institute. To date, the BERD Core Training and Internship Program (BCTIP) has formally trained over 80 students to work on collaborative teams that are integrated throughout the Duke School of Medicine. This manuscript focuses on the setting for the training program, the experiential learning model on which it is based, the structure of the program, and lessons learned to date.

Guidance for biostatisticians on their essential contributions to clinical and translational research protocol review.

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.

Open versus minimally-invasive surgical techniques in pediatric renal tumors: A population-level analysis of in-hospital outcomes.

INTRODUCTION: Minimally-invasive surgery (MIS) has been adopted slowly in pediatric oncology. We attempted to describe contemporary national trends in MIS use; we hypothesized that adolescents (who are more likely to have relatively small renal cell carcinomas) would have a higher proportion of MIS than younger children (who are more likely to have relatively large Wilms tumors) and that this relationship would vary by region. OBJECTIVE: To explore whether pediatric urologic oncology outcomes vary by patient age or by surgical technique. METHODS: We queried the 1998-2014 National Inpatient Sample (NIS) and included encounters in children aged ≤ 18 y, ICD-9 diagnostic code for renal tumor, and procedure code for open or MIS partial or radical nephrectomy. All analyses used weighted descriptive statistics and outcomes are compared based on age group (10 y) or surgery type; Wald-Chi square test was used for differences in proportions and unadjusted weighted ANOVA was used to test for differences in means. RESULTS: 9259 weighted encounters were included; 91% were <10 years old and 50.7% were female. MIS surgery accounted for 1.8% of encounters; there was a difference in proportions by age group (1% <9 y vs. 9.9% >9 y, p < 0.01). The proportion of surgery type was similar across regions within age groups, however. Complications occurred in 13.3% of encounters; mean inpatient length of stay was 8.9 days (SD: 0.3); mean cost was $ 34,457.68 (SD: $1197.00). There was no evidence of a difference between surgery type and proportion of post-operative complications, mean inpatient length of stay or mean inpatient cost. DISCUSSION: The admission-based, retrospective design of NIS left us unable to assess long-term outcomes, repeated admissions, or to track a particular patient across time; this is particularly relevant for oncologic variables on interest such as tumor stage or event-free survival. We were similarly limited in evaluating the effect of pre-surgical referral patterns on patient distributions. CONCLUSION: In this preliminary descriptive analysis, MIS techniques were infrequently used in children, but there was a higher proportion of MIS use among adolescents. There were similar proportions of surgery type across geographic regions within the United States. Whether this infrequent usage is appropriate is as yet unclear given the lack of Level I evidence regarding the relative merits of MIS and open surgery for pediatric and adolescent renal tumors.

Amputation Rates in More Than 175,000 Open Tibia Fractures in the United States.

Open tibia fractures are often associated with considerable soft tissue injuries. Management of open tibia fractures can be challenging, and some patients require amputation. The patient and treatment factors have not been described on a population level in the United States. A retrospective analysis was completed using the 2000 to 2011 Nationwide Inpatient Sample. Amputation rates during the index hospitalization after open tibia fracture were computed based on injury, patient, and hospital characteristics in patients 18 years or older. The overall amputation rate in open tibia fractures during the index hospitalization was 2.2% (n=3769). Patients with midshaft tibia fractures comprised the largest portion of patients undergoing amputation (46.8% of total amputations) compared with distal tibia (34.0%) and proximal tibia (19.3%) fractures. Patients with no neurovascular injury comprised the largest portion of patients undergoing amputation (85.9%), followed by isolated arterial injury (11.1%), combined neurovascular injury (1.9%), and isolated nerve injury (1.1%). Amputation rates were significantly increased for midshaft tibia fractures with neurovascular injury (odds ratio, 12.39; 95% CI, 5.52-27.83) and distal tibia fractures with neurovascular injury (odds ratio, 5.45; 95% CI, 1.73-17.19) compared with tibia fractures with no neurovascular injury while controlling for confounders. On the basis of a review of the Nationwide In-patient Sample during the past decade, the authors have shown that the early amputation rate in open tibia fractures for all-comers is 2.2%. Rates of amputation varied based on fracture site, associated neurovascular injury, medical comorbidities, and hospital location. [Orthopedics. 2021;44(1):48-53.].

Patterns of inpatient care for prostate cancer in men with spina bifida.

BACKGROUND: Advances in medical care have increased the long-term survival of patients with spina bifida. Despite this growing population, limited knowledge is available on age-related illnesses in adults with spina bifida, particularly prostate cancer for which there is no published data. OBJECTIVE: Our aim was to describe inpatient care for prostate cancer in men with spina bifida in the United States. METHODS: We performed a descriptive, retrospective study utilizing the 1998 to 2014 National Inpatient Sample from the Healthcare Cost and Utilization Project. Weights were applied to the sample to make national level inferences. We identified all adult encounters (≥18 years old) with prostate cancer and spina bifida. RESULTS: We identified 253 encounters (mean age 64.9 years). Most were Caucasian (67.5%) and had public insurance (61.6%). 44% of encounters included a major urologic procedure. 38.4% of encounters included prostatectomies, 28.3% included lymph node dissections, and 7.8% included cystectomies. Robotic surgery was performed in 9.4%. Mean length of stay was 5.6 days (95% CI: 3.7, 7.5). The average total cost was $14,074 (95% CI: $8990.3, $19,158.6). CONCLUSIONS: In this first-ever exploration of inpatient care for prostate cancer in men with spina bifida, we found that length of stay and total costs were higher in men with spina bifida. Almost half of encounters included a prostatectomy, cystectomy, and/or lymph node dissection. More detailed investigations are necessary to assess comparative treatment outcomes and complications, including prevalence and mortality rates of prostate cancer among adult men with SB.

Learning gaps among statistical competencies for clinical and translational science learners.

INTRODUCTION: Statistical 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. METHODS: We 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. RESULTS: There 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. CONCLUSION: While 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.

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.

Nationwide Readmission Rates Following Percutaneous Nephrolithotomy: Does Age Matter?

Introduction/Objectives: Despite minimal evidence that evaluates the effect of age on percutaneous nephrolithotomy (PCNL) morbidity, pediatric and elderly patients are considered high-risk groups. Our objective was to assess the effect of the extremes of ages on PCNL readmission and postoperative complication rates. Methods: We identified all PCNL encounters in the 2013 and 2014 Nationwide Readmission Database. Encounters were divided into five age groups: pediatric (<18 years old), young adult (18-25 years old), adult (26-64 years old), geriatric (65-74 years old), and elderly (≥75 years old). Weighted descriptive statistics were used to describe population demographics. We fit an adjusted weighted logistic regression model for 30-day readmission and complication rates. Results: We identified 23,357 encounters. Testing average effect of pediatric and elderly encounters to all other age groups did not reveal a difference in odds for 30-day readmissions, but did result in increased odds for 30-day GU readmissions (odds ratio: 17.7 [95% confidence interval (CI): 2.65-118.9]; p = 0.003). Compared to all other age groups, elderly encounters had 7.5 (95% CI: 2.5-22.7; p = 0.0004) times the odds of a 30-day readmission and 68.3 (95% CI: 29.1-160.4; p < 0.0001) times the odds of a postoperative complication. Conclusions: When comparing the average effect of the extremes of ages to all other age groups, we did not find evidence to suggest a difference in odds for 30-day GU readmissions, but did find increased odds for complications. Further examination revealed that PCNL encounters of elderly patients had significantly increased odds for both readmission and complications, whereas PCNL encounters of pediatric patients did not.

Partial and radical nephrectomy in children, adolescents, and young adults: Equivalent readmissions and postoperative complications.

OBJECTIVES: To compare and contrast the use of partial nephrectomy (PN) and radical nephrectomy (RN) in pediatric malignant renal tumors using a nationally representative database. METHODS: The 2010-2014 Nationwide Readmissions Database (NRD) was used to obtain PN and RN select postoperative data. ICD-9-CM codes were used to identify children (<10 years), adolescents (10-19 years) and young adults (20-30 years) diagnosed with malignant renal tumors who were treated with a PN or RN. The presence of a 30-day readmission, occurrence of postoperative complications, cost, and length of stay (LOS) were studied and weighted logistic regression models were fit to test for associations. RESULTS: There were 4330 weighted encounters (1289 PNs, 3041 RNs) that met inclusion criteria: 50.8% were children, 7.2% were adolescents, and 42% were young adults. Young adults had the highest rates of PN, whereas children had the highest rates of RN (p < 0.0001). Overall, no evidence was found to suggest a difference in odds between surgical modality and the presence of a 30-day readmission or postoperative complication. While PN was on average $9000 cheaper compared to RN overall, its cost was similar to that of RN for children. Similarly, PN patients had a shorter overall LOS compared to RN patients, but their LOS was similar to that of children who underwent RN. CONCLUSION: There was no evidence of a difference in odds between RN and PN in terms of postoperative readmissions or in-hospital complication rates. Additionally, we observed descriptive differences in both cost and LOS between the surgical modalities across age groups. TYPE OF STUDY: Retrospective comparative study (administrative database analysis). LEVEL OF EVIDENCE: Level III.