Death of the P Value? Bayesian Statistics for Orthopaedic Surgeons

Area of Science:

• Biostatistics
• Evidence-Based Medicine
• Clinical Trial Design

Background:

• Statistical interpretation is crucial for evidence-based medicine, with frequentist (P value testing) and Bayesian statistics being primary hypothesis testing methods.
• Bayesian methods have seen a significant increase in use over the last decade, indicating a growing interest in alternative statistical approaches.
• Frequentist methods, while common, suffer from widespread misunderstanding and misuse, particularly concerning the interpretation of P values.

Purpose of the Study:

• To introduce fundamental Bayesian principles and Bayes' theorem.
• To illustrate how pretest probability and existing information can guide diagnostic testing, using prosthetic joint infection as a case study.
• To contrast Bayesian and frequentist methodologies with an example from the VANCO orthopaedic prospective trial and outline criteria for critically appraising Bayesian studies.

Main Methods:

• Review of Bayesian principles and Bayes' theorem.
• Application of Bayesian concepts to diagnostic testing using a prosthetic joint infection example.
• Comparative analysis of Bayesian and frequentist approaches using data from the VANCO orthopaedic prospective trial.

Main Results:

• Bayesian approaches provide a framework for interpreting smaller, clinically relevant differences that may be missed by P value thresholds.
• The Bayesian method allows for the incorporation of prior knowledge, refining diagnostic accuracy and clinical decision-making.
• While frequentist P values can lead to dichotomous conclusions, Bayesian methods offer a more nuanced interpretation of evidence.

Conclusions:

• Bayesian statistics present a robust alternative to frequentist methods, enhancing the interpretation of clinical trial data and diagnostic testing.
• Understanding Bayesian principles is essential for critically evaluating studies and advancing evidence-based medicine.
• The Bayesian approach supports a more intuitive alignment with clinical decision-making processes, especially when dealing with marginal findings.