Bibliometric Analysis of Surgical Articles Using Bayesian Statistics.

Abstract

Objectives: The study aims to investigate the landscape and trends in the use of Bayesian statistics in surgical papers published in high-impact journals over the past 2 decades, determine the characteristics of these papers, and assess the quality of Bayesian analysis reporting.

Background: Observational and clinical trials have traditionally employed frequentist approaches. Bayesian framework enables the incorporation of prior evidence, flexible modeling of uncertainty, and returns a direct probabilistic summary of the estimates of interest that can provide valuable insight. However, their use in high-impact surgical research remains underexplored.

Methods: Surgical articles from high-impact surgical and medical journals indexed in Web of Science and PubMed were retrieved for the period from January 2000 to August 2024. Data extraction covered bibliometrics and content details. The Reporting of Bayes Used in Clinical Studies scale (ROBUST) was used to assess Bayesian reporting quality.

Results: A total of 120 articles were analyzed. The use of Bayesian statistics in surgical research has increased over time (compounded annual growth rate: 12.3%). General surgery (N = 39, 32.5%) and cardiothoracic surgery (N = 20, 16.7%) were the most represented specialties. The most common study designs were retrospective cohort studies (N = 50, 41.7%), meta-analyses (N = 38, 31.7%), and randomized trials (N = 19, 15.8%). Regression-based methods were the most frequently used (N = 51, 42.5%). The average ROBUST score was 4.1 ± 1.6 out of 7, with 54.0% (N = 54) of studies specifying priors and 29.0% (N = 29) justifying them.

Conclusions: Bayesian statistics is increasingly incorporated into surgical research, predominantly observational studies and meta-analyses. However, improvements in the quality and standardization of Bayesian reporting are needed to enhance transparency and reproducibility.