{"doi":"10.1017/cts.2023.689","title":"A practical guide to adopting Bayesian analyses in clinical research","abstract":"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.","journal":"Journal of Clinical and Translational Science","year":2023,"id":358830,"datarank":0.37273599746820013,"base_score":2.4849066497880004,"endowment":2.4849066497880004,"self_citation_contribution":0.37273599746820013,"citation_network_contribution":0.0,"self_endowment_contribution":0.37273599746820013,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":11,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9704,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":347985,"name":"Edward J. Bedrick","orcid":"0000-0002-4215-795X","position":1,"is_corresponding":false},{"id":1109197,"name":"Jacob Hutchins","orcid":"0000-0003-0996-4175","position":2,"is_corresponding":false},{"id":1109198,"name":"Aaron A. Berg","orcid":"0000-0002-6671-962X","position":3,"is_corresponding":false},{"id":303666,"name":"Alexander Kaizer","orcid":"0000-0003-2334-5514","position":4,"is_corresponding":false},{"id":519059,"name":"Nichole E. Carlson","orcid":"0000-0003-0679-6663","position":5,"is_corresponding":false},{"id":1109196,"name":"Lauren B. Gunn-Sandell","orcid":"0009-0001-4673-2114","position":0,"is_corresponding":true}],"reference_count":47,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-19T01:13:48.379546Z","pmid":"38384916","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}