The Value of Bayesian Statistics in Prostate Cancer Research: A Clinician-Oriented Review

Bayesian statistics provide direct probabilistic interpretations that align with clinical decision-making in prostate cancer. Unlike frequentist approaches, which yield P-values and confidence intervals, Bayesian methods generate posterior probabilities that directly address patient-facing questions. This review synthesizes Bayesian applications across the prostate cancer continuum. We conducted a narrative review informed by a structured search of PubMed, Web of Science, and the Cochrane Library. Searches yielded 465 records; after removal of 144 duplicates, 321 were screened, and 11 representative studies were selected. The review was prepared with reference to the Scale for the Assessment of Narrative Review Articles (SANRA) guidance. Bayesian methods produced clinically interpretable probabilities and individualized predictions across prostate cancer care. Joint modeling of longitudinal prostate-specific antigen with time-to-event outcomes enabled dynamic recurrence forecasting, and risk-triggered active surveillance schedules reduced biopsies while maintaining timely upgrading detection. Bayesian network meta-analyses ranked systemic therapies and imaging tracers. Adaptive, model-based designs optimized radiotherapy and radioligand dosing, while population pharmacokinetic/pharmacodynamic frameworks supported hypothesis-generating personalization. Bayesian networks also improved causal adjustment in observational studies. Collectively, these applications enhanced evidence synthesis, decision transparency, patient communication, and model calibration. Bayesian statistics complement frequentist methods by providing interpretable probabilities, incorporating prior knowledge, and supporting transparent clinical decisions. Familiarity with key concepts-posterior probabilities, credible intervals, prior specification, and calibration-enables clinicians to critically appraise Bayesian studies and communicate results effectively. As prostate cancer management increasingly emphasizes longitudinal monitoring and personalized treatment, Bayesian methods offer natural frameworks for evidence synthesis and decision-making under uncertainty.