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.

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.

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.

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.

To examine how food insecurity is related to emerging adults’ food behaviours and experiences of neighbourhood safety and discrimination and to identify resources needed to support their health during the COVID-19 outbreak.

Rapid response online survey. Participants completed the six-item US Household Food Security Survey Module, a brief measure of food insufficiency, and measures of food behaviours, neighbourhood safety and discrimination. Open-ended questions were used to assess changes in eating behaviours during COVID-19 and needed resources.

C-EAT (COVID-19 Eating and Activity over Time) study invitations were sent by email and text message to a longitudinal cohort.

A total of 218 emerging adults (mean age = 24·6 (sd 2·0) years, 70·2 % female) completed a survey in April–May 2020 during a stay-at-home order in Minnesota.

The past year prevalence of food insecurity was 28·4 %. Among food-insecure respondents, 41·0 % reported both eating less and experiencing hunger due to lack of money in the past month. Food-insecure respondents were less likely than those who were food secure to have fruits/vegetables at home and more likely to have frequent fast-food restaurant meals, feel unsafe in their neighbourhood and experience discrimination during the stay-at-home order. Food-insecure adults reported changes including eating more food prepared at home, eating more take-out restaurant meals and purchasing more energy-dense snacks as a result of events related to COVID-19. Resources most needed to support their health included eligibility for more food assistance and relief funds.

Food-insecure emerging adults experience many barriers to maintaining healthful eating patterns during COVID-19.