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.

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.

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.

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.

Many women experience both vasomotor menopausal symptoms (VMS) and depressed mood at midlife, but little is known regarding the prospective bi-directional relationships between VMS and depressed mood and the role of sleep difficulties in both directions.

A pooled analysis was conducted using data from 21 312 women (median: 50 years, interquartile range 49−51) in eight studies from the InterLACE consortium. The degree of VMS, sleep difficulties, and depressed mood was self-reported and categorised as never, rarely, sometimes, and often (if reporting frequency) or never, mild, moderate, and severe (if reporting severity). Multivariable logistic regression models were used to examine the bi-directional associations adjusted for within-study correlation.

At baseline, the prevalence of VMS (40%, range 13–62%) and depressed mood (26%, 8–41%) varied substantially across studies, and a strong dose-dependent association between VMS and likelihood of depressed mood was found. Over 3 years of follow-up, women with often/severe VMS at baseline were more likely to have subsequent depressed mood compared with those without VMS (odds ratios (OR) 1.56, 1.27–1.92). Women with often/severe depressed mood at baseline were also more likely to have subsequent VMS than those without depressed mood (OR 1.89, 1.47–2.44). With further adjustment for the degree of sleep difficulties at baseline, the OR of having a subsequent depressed mood associated with often/severe VMS was attenuated and no longer significant (OR 1.13, 0.90–1.40). Conversely, often/severe depressed mood remained significantly associated with subsequent VMS (OR 1.80, 1.38–2.34).

Difficulty in sleeping largely explained the relationship between VMS and subsequent depressed mood, but it had little impact on the relationship between depressed mood and subsequent VMS.