A practical guide to adopting Bayesian analyses in clinical research.

Area of Science:

• Biostatistics
• Clinical Research Methodology

Background:

• Bayesian statistical methods are underutilized in clinical and translational (C&T) research despite their prevalence in statistical methodology.
• There is a need to enhance C&T researchers' confidence in interpreting and implementing Bayesian analyses.

Purpose of the Study:

• To accelerate the adoption of new Bayesian statistical methods in C&T research.
• To provide practical guidance and examples for applying Bayesian analysis in C&T settings.

Main Methods:

• Developed and implemented a Bayesian data analysis plan for a two-arm clinical trial.
• Compared the effectiveness of a new opioid for post-operative pain management.
• Demonstrated the application of four Bayesian statistical packages (STATA, SAS, RStan) for linear and logistic regression.

Main Results:

• Bayesian analysis results were robust across different statistical packages and prior distributions.
• STATA was user-friendly for linear regression but had limitations in model fitting and summarization.
• SAS and R provided better documentation and data management for posterior analysis, with greater flexibility for complex models.

Conclusions:

• Bayesian analysis is now accessible to a wider range of data analysts.
• Bayesian methodology should be more widely considered in C&T research for complex problems.
• C&T research teams can benefit from adopting and interpreting Bayesian approaches for advanced analyses.