Post-Davidson’s – ROC graphs and medical statistics
The much awaited 20th ed of Davidson’s Principles & Practice of Medicine is here with a bang and besides a host of new additions the book boldly claims to have taken the content up a notch – both in terms of depth and breadth, keeping the needs of the MRCP examinee especially borne in mind. The very first chapter, Good Medical Practice is a welcome addition and will likely provide the new generation/edition reader an edge over his or her senior counterparts. Studded with key topics in medical ethics, basic biostatistics and Baye’s theorem besides an array of handy tips pertaining to the everyday practice of medicine, this chapter fits like a necklace around the other contents of the book. The logic behind restricting the number of Dx tests to a reasonable minimum inorder to decrease the over-all false-positives is just one example of what this treasure trove holds. Readers interested in medical ethics will want to use Kumar & Clark’s Clinical Medicine 6ed to fill any minor gaps, particularly things such as the medical relevance of the European Convention on Human Rights, etc.
The trade-off between sensitivity and specificity has been depicted using a Reciever Operator Characteristic graph which in itself is supposed to be a sophisticated biostatistical concept. Readers will find it interesting to note the following about the history of the ROC curve, courtesy Wikipedia :-
“….ROC curves are used to evaluate the results of a prediction and were first employed in the study of discriminator systems for the detection of radio signals in the presence of noise in the 1940s, following the attack on Pearl Harbor. The initial research was motivated by the desire to determine how the US RADAR “receiver operators” had missed the Japanese aircraft.
In the 1960s they began to be used in psychophysics, to assess human (and occasionally animal) detection of weak signals. They also proved to be useful for the evaluation of machine learning results, such as the evaluation of Internet search engines….”
ROC curves are extensively used under the domain of the signal detection theory.
Notice that Davidson’s ROC curve plots sensitivity vs. specificity and that the same curve can be obtained by plotting sensitivity vs. (1-specificity), only the sequence of the numbering about the x and y axes needs re-arrangement.
“A simple example of a ROC curve” is maintained by The University of Nebraska Medical Center and quite succintly explains the entire dynamics of an ROC curve.