Ethical Counselling and Medical Decision-Making in the Era of Personalised Medicine by Giovanni Boniolo & Virginia Sanchini

Author:Giovanni Boniolo & Virginia Sanchini
Language: eng
Format: epub
Publisher: Springer International Publishing, Cham

Actually, these probabilities are meaningful only with respect to the prevalence of the supposed disease in the population of interest: another datum that the clinician should know. The higher the prevalence of the disease in the considered population is, the higher the positive predictive value (and the number of positive cases detected, i.e. the yield) is. Note that while sensitivity and specificity are characteristics of the test, positive and negative predictive values depend on the prevalence of the disease in the population of interest.

There is, however, a better way of putting all together both the information we have (on the test and on the population of interest) and the correlation between positive/negative predictive value and prevalence. This way is offered by the powerful and elegant Bayes theorem.

It is not relevant here to know how Bayes theorem has been formulated and proved. Instead, it is important that it allows the clinician to connect the data concerning the sensitivity and the specificity of the test with the prevalence of the disease in an explicit manner. Moreover, it is a formal tool that permits to move from the pathological level concerning the knowledge of the effects given the causes (synthesized in P(T +/D)) to the clinical level concerning the knowledge of the causes given the effects (synthesized in P(D/T +)).

Abstractedly, it claims that

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