Saturday, October 11, 2014

Calling Dr Data-Dr Null-Dr Data for Evidence-Based Medicine.

anonymizer
"Dr. Data" – likely for SEO reasons it has yet another name in its online version – asks if statistical analysis of databases of historical medical data can be more useful than clinical trial data for diagnosing patients. It recounts the story of Dr. Jennifer Frankovich, a Stanford Children's Hospital rheumatologist, who encountered the young girl symptoms of kidney failure, with possible lupus. Frankovich suspected blood clotting issues, but had to research the matter in order to convince colleagues.  Scientific literature comprising clinical trial data did not offer clues. Instead, Frankovich found evidence of connection of clotting and lupus, given certain circumstances, by searching a database of lupus patients that had been to this hospital over the last five years.

The story by Veronique Greenwood tells us she wrote of her experience in a letter to the New England Journal of Medicine, and was subsequently warned by her bosses not to do that kind of query again. Assumedly HIPPA privacy concerns are involved.

It stands to reason that data on all the medical cases in any given hospital could have some good use. It leads me to wonder. Shouldn’t the 'anonymouzation' or masking of individuals' identities within such databanks be a priority? Is the hegemony of the clinical trial era due to ebb, especially when taking into account the momentum of the World Wide Web?

Frankovich's work could come under the aegis of Evidence-Based Medicine. The expanded Web-borne appoach a'brewing here is sometimes called Digital Experimentation. –Jack Vaughan

Saturday, October 4, 2014

Bayesian Blues

Bayesian methods continue to gain attention as a better means to solve problems and predict outcomes. It was Google that used such algorithms to tune its brilliant search engine, and much more. Nate Silver carried the Bayesian chorus along with his depiction of the method in "The Signal and the Noise." Today, in fact, Bayesian thinking is very broadly adapted - enough so for the New York Times to devote a feature entitled "The Odds, Continually Updated" in this week's Science Times section.

Thomas Bayes writer F.T. Flam [I am not making this up] says set out to calculate the probability of God's existence. This was back in the 19th Century in jolly old England. The math was difficult and really beyond the ken of calculastion - until the recent profusion of clustered computer power. Almost a MacGuffin in the narrative is the overboard Long Island fisherman John Aldrich who the Coast Guard found in the Atlantic Ocean to that services use of Bayesian methods.

"The Odds, Continually Updated'' places more import on the possibility that Bayesian statistics have narrowed down the possible correct answers for the age of the earth (from existing estimations that it was 8 B to 15 B years old, to conjectures that it is 13.8 B years old. - Jack Vaugahn


An extended version* of this piece would consider:
Who was Bayes and what is Bayesian math?
How does it compare to frequentist statistics (the main point of the story)? If frequentist methods were aptly applied would they work in most cases?
How does this all relate to the looming question (smaller)of how much we can  trust data science and (bigger) how much we can trust science?

*for now this site like most blogs comprises free ruminations - and sometimes you get what you pay for.