What makes a biostatistician?

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.

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.

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.

Involvement of information specialists and statisticians in systematic reviews

BACKGROUND

Systematic reviews (SRs) are usually conducted by a highly specialized group of researchers. The routine involvement of methodological experts is a core methodological recommendation. The present commentary describes the qualifications required for information specialists and statisticians involved in SRs, as well as their tasks, the methodological challenges they face, and potential future areas of involvement.

TASKS AND QUALIFICATIONS

Information specialists select the information sources, develop search strategies, conduct the searches, and report the results. Statisticians select the methods for evidence synthesis, assess the risk of bias, and interpret the results. The minimum requirements for their involvement in SRs are a suitable university degree (e.g., in statistics or librarian/information science or an equivalent degree), methodological and content expertise, and several years of experience.

KEY ARGUMENTS

The complexity of conducting SRs has greatly increased due to a massive rise in the amount of available evidence and the number and complexity of SR methods, largely statistical and information retrieval methods. Additional challenges exist in the actual conduct of an SR, such as judging how complex the research question could become and what hurdles could arise during the course of the project.

CONCLUSION

SRs are becoming more and more complex to conduct and information specialists and statisticians should routinely be involved right from the start of the SR. This increases the trustworthiness of SRs as the basis for reliable, unbiased and reproducible health policy, and clinical decision making.

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.

Reporting Standards for a Bland-Altman Agreement Analysis: A Review of Methodological Reviews

The Bland-Altman Limits of Agreement is a popular and widespread means of analyzing the agreement of two methods, instruments, or raters in quantitative outcomes. An agreement analysis could be reported as a stand-alone research article but it is more often conducted as a minor quality assurance project in a subgroup of patients, as a part of a larger diagnostic accuracy study, clinical trial, or epidemiological survey. Consequently, such an analysis is often limited to brief descriptions in the main report. Therefore, in several medical fields, it has been recommended to report specific items related to the Bland-Altman analysis. The present study aimed to identify the most comprehensive and appropriate list of items for such an analysis. Seven proposals were identified from a MEDLINE/PubMed search, three of which were derived by reviewing anesthesia journals. Broad consensus was seen for the a priori establishment of acceptability benchmarks, estimation of repeatability of measurements, description of the data structure, visual assessment of the normality and homogeneity assumption, and plotting and numerically reporting both bias and the Bland-Altman Limits of Agreement, including respective 95% confidence intervals. Abu-Arafeh et al. provided the most comprehensive and prudent list, identifying 13 key items for reporting (Br. J. Anaesth. 2016, 117, 569-575). An exemplification with interrater data from a local study accentuated the straightforwardness of transparent reporting of the Bland-Altman analysis. The 13 key items should be applied by researchers, journal editors, and reviewers in the future, to increase the quality of reporting Bland-Altman agreement analyses.

Why do you need a biostatistician?

The quality of medical research importantly depends, among other aspects, on a valid statistical planning of the study, analysis of the data, and reporting of the results, which is usually guaranteed by a biostatistician. However, there are several related professions next to the biostatistician, for example epidemiologists, medical informaticians and bioinformaticians. For medical experts, it is often not clear what the differences between these professions are and how the specific role of a biostatistician can be described. For physicians involved in medical research, this is problematic because false expectations often lead to frustration on both sides. Therefore, the aim of this article is to outline the tasks and responsibilities of biostatisticians in clinical trials as well as in other fields of application in medical research.

Comprehensive survey among statistical members of medical ethics committees in Germany on their personal impression of completeness and correctness of biostatistical aspects of submitted study protocols

OBJECTIVES

To assess biostatistical quality of study protocols submitted to German medical ethics committees according to personal appraisal of their statistical members.

DESIGN

We conducted a web-based survey among biostatisticians who have been active as members in German medical ethics committees during the past 3 years.

SETTING

The study population was identified by a comprehensive web search on websites of German medical ethics committees.

PARTICIPANTS

The final list comprised 86 eligible persons. In total, 57 (66%) completed the survey.

QUESTIONNAIRE

The first item checked whether the inclusion criterion was met. The last item assessed satisfaction with the survey. Four items aimed to characterise the medical ethics committee in terms of type and location, one item asked for the urgency of biostatistical training addressed to the medical investigators. The main 2×12 items reported an individual assessment of the quality of biostatistical aspects in the submitted study protocols, while distinguishing studies according to the German Medicines Act (AMG)/German Act on Medical Devices (MPG) and studies non-regulated by these laws.

PRIMARY AND SECONDARY OUTCOME MEASURES

The individual assessment of the quality of biostatistical aspects corresponds to the primary objective. Thus, participants were asked to complete the sentence 'In x% of the submitted study protocols, the following problem occurs', where 12 different statistical problems were formulated. All other items assess secondary endpoints.

RESULTS

For all biostatistical aspects, 45 of 49 (91.8%) participants judged the quality of AMG/MPG study protocols much better than that of 'non-regulated' studies. The latter are in median affected 20%-60% more often by statistical problems. The highest need for training was reported for sample size calculation, missing values and multiple comparison procedures.

CONCLUSIONS

Biostatisticians being active in German medical ethics committees classify the biostatistical quality of study protocols as low for 'non-regulated' studies, whereas quality is much better for AMG/MPG studies.

[The role of biostatistics in institutional review boards]

Research in humans is associated with risks. These risks are only justifiable if an independent institutional review board (IRB) has evaluated the planned research in terms of scientific integrity. Only scientifically sound research can be considered ethical. A biostatistician should be a member of the IRB to assure adequate evaluation of fundamental topics like design, sample size estimation, and statistical analysis of the study.This paper presents core biostatistical concepts following the current guidelines of the International Council of Harmonization (ICH E6 and ICH E9). We discuss important pitfalls based on examples from published clinical trials. Furthermore, we discuss new concepts like estimands and their relevance for biostatisticians working in IRBs. Finally, we discuss the role of biostatisticians in IRBs and present thoughts on the way they should be trained.