Reverse-Bayes methods for evidence assessment and research synthesis

Area of Science:

• Statistical inference
• Bayesian methods
• Scientific research methodology

Background:

• Null-hypothesis significance testing (NHST) is widely recognized as insufficient for robust scientific inference.
• The scientific community lacks consensus on alternative evidence assessment methods, with Bayesian approaches facing persistent challenges.
• The "problem of priors" in Bayesian analysis has historically hindered its widespread adoption.

Purpose of the Study:

• To introduce and elaborate on Reverse-Bayes analysis as a solution to the "problem of priors" in Bayesian inference.
• To demonstrate how Reverse-Bayes methods can enhance the assessment of scientific findings and research synthesis.
• To provide a practical framework for utilizing Bayesian methods more effectively in scientific research.

Main Methods:

• Reverse-Bayes analysis, which deduces priors from the likelihood by setting a required posterior credibility level.
• Application of Reverse-Bayes methods to address inferential challenges including credibility assessment, contextualization of findings, replication probability estimation, and improved interpretation of NHST.
• Utilizing a meta-analysis of randomized controlled trials on corticosteroids for COVID-19 patients as a case study.

Main Results:

• Reverse-Bayes analysis provides a method to overcome the "problem of priors" by reversing the conventional Bayesian framework.
• This approach facilitates the assessment of scientific findings' credibility and aids in research synthesis.
• The methods offer enhanced insights from NHST while mitigating misinterpretation risks.

Conclusions:

• Reverse-Bayes methods offer a more accessible and attractive pathway for implementing Bayesian inference in scientific research.
• This approach can significantly improve evidence assessment, research synthesis, and the overall reliability of scientific conclusions.
• The framework presented has broad applicability across various scientific disciplines facing inferential challenges.