Death of the P Value? Bayesian Statistics for Orthopaedic Surgeons

Statistical interpretation is foundational to evidence-based medicine. Frequentist (P value testing) and Bayesian statistics are two major approaches for hypothesis testing. Studies analyzed with Bayesian methods are increasingly common with a 4-fold increase in the past 10 years. The Bayesian approach can align with clinical decision making by interpreting smaller differences that are not limited by P values and misleading claims of "trends toward significance." Both methods follow a workflow that includes sampling, hypothesis testing, interpretation, and iteration. Frequentist methodology is familiar and common. However, the limitations are the misunderstanding, misuse, and deceptively simple utility of interpreting dichotomous P values. Bayesian approaches are relatively less common and provide an alternative approach to trial design and data interpretation. Marginal differences elucidated by Bayesian methods may be perceived as less decisive than a P value that may reject a null hypothesis. The purposes of this review are to introduce Bayesian principles and Bayes theorem, define how pretest probability and known information may inform diagnostic testing using an example from prosthetic joint infection, contrast Bayesian and frequentist approaches using an example from the VANCO orthopaedic prospective trial, and describe the criteria for critically reviewing Bayesian studies.