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.

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.

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.