Assessing statistical competencies in clinical and translational science education: one size does not fit all

A practical guide to adopting Bayesian analyses in clinical research

Background

Bayesian statistical approaches are extensively used in new statistical methods but have not been adopted at the same rate in clinical and translational (C&T) research. The goal of this paper is to accelerate the transition of new methods into practice by improving the C&T researcher's ability to gain confidence in interpreting and implementing Bayesian analyses.

Methods

We developed a Bayesian data analysis plan and implemented that plan for a two-arm clinical trial comparing the effectiveness of a new opioid in reducing time to discharge from the post-operative anesthesia unit and nerve block usage in surgery. Through this application, we offer a brief tutorial on Bayesian methods and exhibit how to apply four Bayesian statistical packages from STATA, SAS, and RStan to conduct linear and logistic regression analyses in clinical research.

Results

The analysis results in our application were robust to statistical package and consistent across a wide range of prior distributions. STATA was the most approachable package for linear regression but was more limited in the models that could be fitted and easily summarized. SAS and R offered more straightforward documentation and data management for the posteriors. They also offered direct programming of the likelihood making them more easily extendable to complex problems.

Conclusion

Bayesian analysis is now accessible to a broad range of data analysts and should be considered in more C&T research analyses. This will allow C&T research teams the ability to adopt and interpret Bayesian methodology in more complex problems where Bayesian approaches are often needed.

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.

The Importance of Statistical Competencies for Medical Research Learners

It is very important for medical professionals and medical researchers to be literate in statistics. However, we have found that the degree of literacy that is required should not be identical for every statistical competency or even for every learner. We first begin by describing why the development, teaching, and assessment of statistical competencies for medical professionals and medical researchers are critical tasks. We next review our three substantial efforts at developing a comprehensive list of statistical competencies that can be used as a guide for what medical research learners should know about statistics, for curricular development, and for assessment of statistical education. We then summarize the origin and the inclusion of the statistical competency items. We follow this with a description of potential uses and applications of the statistical competencies to improve targeted learning for medical research learners. Finally, we discuss implications of the statistical competencies for undergraduate statistics education.

Implementation of a competency-based medical education approach in public health and epidemiology training of medical students

Background

There is increasing agreement among medical educators regarding the importance of improving the integration between public health and clinical education, understanding and implementation of epidemiological methods, and the ability to critically appraise medical literature. The Sackler School of Medicine at Tel-Aviv University revised its public health and preventive medicine curriculum, during 2013–2014, according to the competency-based medical education (CBME) approach in training medical students. We describe the revised curriculum, which aimed to strengthen competencies in quantitative research methods, epidemiology, public health and preventive medicine, and health service organization and delivery.

Methods

We report the process undertaken to establish a relevant 6-year longitudinal curriculum and describe its contents, implementation, and continuous assessment and evaluation.

Results

Central competencies included: epidemiology and statistics for appraisal of the literature and implementation of research; the application of health promotion principles and health education strategies in disease prevention; the use of an evidence-based approach in clinical and public health decision making; the examination and analysis of disease trends at the population level; and knowledge of the structure of health systems and the role of the physician in these systems. Two new courses, in health promotion, and in public health, were added to the curriculum, and the courses in statistics and epidemiology were joined. Annual evaluation of each course results in continuous revisions of the syllabi as needed, while we continue to monitor the whole curriculum.

Conclusions

The described revision in a 6 year-medical school training curriculum addresses the currently identified needs in public health. Ongoing feedback from students, and re-evaluation of syllabus by courses teams are held annually. Analysis of student’s written feedbacks and courses evaluations of “before and after” the implementation of this intervention is taking place to examine the effect of the new curriculum on the perceived clinical and research capacities of our 6-year students.

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.

Reinventing Biostatistics Education for Basic Scientists

Numerous studies demonstrating that statistical errors are common in basic science publications have led to calls to improve statistical training for basic scientists. In this article, we sought to evaluate statistical requirements for PhD training and to identify opportunities for improving biostatistics education in the basic sciences. We provide recommendations for improving statistics training for basic biomedical scientists, including: 1. Encouraging departments to require statistics training, 2. Tailoring coursework to the students’ fields of research, and 3. Developing tools and strategies to promote education and dissemination of statistical knowledge. We also provide a list of statistical considerations that should be addressed in statistics education for basic scientists.

How is biostatistics education failing to meet the needs of basic scientists, and what can we do to fix this? This Perspective discusses strategies for making biostatistics an integral part of postgraduate and continuing education for basic scientists, ensuring that courses teach the specific skills needed to design, analyze, and critically evaluate basic science research.

Beyond bar and line graphs: time for a new data presentation paradigm

Figures in scientific publications are critically important because they often show the data supporting key findings. Our systematic review of research articles published in top physiology journals (n = 703) suggests that, as scientists, we urgently need to change our practices for presenting continuous data in small sample size studies. Papers rarely included scatterplots, box plots, and histograms that allow readers to critically evaluate continuous data. Most papers presented continuous data in bar and line graphs. This is problematic, as many different data distributions can lead to the same bar or line graph. The full data may suggest different conclusions from the summary statistics. We recommend training investigators in data presentation, encouraging a more complete presentation of data, and changing journal editorial policies. Investigators can quickly make univariate scatterplots for small sample size studies using our Excel templates.

A systematic review of research articles reveals widespread poor practice in the presentation of continuous data. The authors recommend training for investigators and supply templates for easy use.