Bayesian interpretation of likelihood ratios in clinical diagnosis and public health

Abstract

Purpose of review This study provides a Bayesian interpretation of likelihood ratios and their role in updating the pretest probability of disease, typically represented by the prevalence, to a posttest probability. Recent findings We highlight the importance of likelihood ratios in this process, particularly in low prevalence settings. For example, we demonstrate that a high LR+ can significantly increase the posttest probability of disease, providing crucial information for clinical decision-making. Furthermore, we emphasize the significance of the fold increase in the probability of disease given a positive test result, particularly in low prevalence settings. Summary This study underscores the importance of considering likelihood ratios on our posttest updated probability of disease and highlights their implications for public health and individual diagnosis. Understanding these factors can enhance the accuracy and effectiveness of diagnostic decision-making, ultimately leading to improved public health outcomes and patient care.