There are two main schools of thought about statistical inference: frequentist and Bayesian. The frequentist approach relies solely on available data for predictions, while the Bayesian approach incorporates both data and prior knowledge about the event of interest. Bayesian methods were developed hundreds of years ago; however, they were rarely used due to computational challenges and conflicts between the two schools of thought. Recent advances in computational capabilities and a shift toward leveraging prior knowledge for inferences have led to increased use of Bayesian methods.

Many biostatisticians with expertise in frequentist approaches lack the skills to apply Bayesian techniques. To address this gap, four faculty experts in Bayesian modeling at the University of Michigan developed a practical, customized workshop series. The training, tailored to accommodate the schedules of full-time staff, focused on immersive, project-based learning rather than traditional lecture-based methods. Surveys were conducted to assess the impact of the program.

All 20 participants completed the program and when surveyed reported an increased understanding of Bayesian theory and greater confidence in using these techniques. Capstone projects demonstrated participants’ ability to apply Bayesian methodology. The workshop not only enhanced the participants’ skills but also positioned them to readily apply Bayesian techniques in their work.

Accommodating the schedules of full-time biostatistical staff enabled full participation. The immersive project-based learning approach resulted in building skills and increasing confidence among staff statisticians who were unfamiliar with Bayesian methods and their practical applications.

The Clinical and Translational Science Award (CTSA) Program is a Consortium of nearly 60 academic medical research centers across the USA and a natural network for evaluating the spread and uptake of translational research innovation across the Consortium.

Dissemination of the Accrual to Clinical Trials (ACT) Network, a federated clinical informatics data network for population-based cohort discovery, began January 2018 across the Consortium. Diffusion of innovation theory guided dissemination design and evaluation. Mixed-methods assessed the spread and uptake across the Consortium through July 1, 2019 (n = 48 CTSAs). Methods included prospective time activity tracking (Kaplan–Meier curves), and survey and qualitative interviews.

Within 18 months, nearly 80% of CTSAs had joined the data network and two-thirds of CTSAs achieving technical readiness had initiated launch to local clinical investigators. Over 10,000 ACT Network queries are projected for 2019; and by 2020, nearly all CTSAs will have joined the network. Median time-from-technical-readiness-to-local-launch was 154 days (interquartile range: 87–225 days]. Quality improvement processes reduced time-to-launch by 35.2% (64 days, p = 0.0036). Lessons learned include: (1) conceptualize dissemination as two-stage adoption demonstrating value for both CTSA hub service providers and clinical investigators; (2) include institutional trial into dissemination strategies so CTSA hubs can refine internal workflows and gather local user feedback endorsement; (3) embrace designing-for-dissemination during technology development; and (4) sustain adaptive dissemination and customer relationship management to keep CTSA hubs and users engaged.

Scale-up and spread of the ACT Network provides lessons learned for others disseminating innovation across the CTSA Consortium. The Network is primed for embedded implementation research.

Previous studies have examined the impact of Clinical and Translational Science Awards programs on other outcomes, but not on grant seeking. The authors examined the effects on grant seeking of the Michigan Institute for Clinical & Health Research (MICHR), a Clinical and Translational Science Awards institute at the University of Michigan.

We assessed over 63,000 grant proposals submitted at the University of Michigan in the years 2002–2012 using data from the university and MICHR’s Tracking Metrics and Reporting System. We used a retrospective, observational study of the dynamics of grant-seeking success and award funding. Heckman selection models were run to assess MICHR’s relationship with a proposal’s success (selection), and subsequently the award’s size (outcome). Models were run for all proposals and for clinical and translational research (CTR) proposals alone. Other covariates included proposal classification, type of grant award, academic unit, and year.

MICHR had a positive and statistically significant relationship with success for both proposal types. For all grants, MICHR was associated with a 29.6% increase in award size. For CTR grants, MICHR had a statistically nonsignificant relationship with award size.

MICHR’s infrastructure, created to enable and enhance CTR, has also created positive spillovers for a broader spectrum of research and grant seeking.