Invited Commentary

Commentary on “Propagation of Uncertainty in Bayesian Diagnostic Test Interpretation”

Authors: Lawrence E. Frisch, MD, MPH, Kathryn Morrison, MSc

Abstract

The current issue of the Southern Medical Journal features an article by Bianchi and colleagues that adds to our understanding of the clinical use of likelihood ratios (LRs).1 Likelihood ratios have been proposed as a tool allowing clinicians to use Bayes’ theorem at the bedside. Our purpose in this commentary is to suggest three reasons why readers may find Bianchi and colleagues’ focus on Bayesian thinking to be of importance:


1. Bayesian analytic techniques are nearly universally used in genome studies and are emerging as attractive options in certain kinds of clinical trials.


2. The Bayesian perspective helps clinicians to use diagnostic testing more skillfully.


3. Bayesian methods allow us to interpret studies in ways that seem more intuitive than P values and confidence limits.

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References

1. Srinivasan P, Westover MB, Bianchi MT. Propagation of uncertainty in Bayesian diagnostic test interpretation. South Med J 2012; 105: 452–459.
 
2. Adamina M, Tomlinson G, Guller U. Bayesian statistics in oncology: a guide for the clinical investigator. Cancer 2009; 115: 5371–5381.
 
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9. Kahneman D. Maps of bounded rationality: a perspective on intuitive judgment and choice. Nobel Prize lecture, 2002. http://www.nobelprize.org/nobel_prizes/economics/laureates/2002/kahnemann-lecture.pdf. Accessed March 17, 2012.
 
10. Berner ES, Graber ML. Overconfidence as a cause of diagnostic error in medicine. Am J Med 2008; 121: S2–S23.