Estatística Baeysiana no New York Times

[gjaa@de.ufpe.br]

Caros Colegas,

Gostei muito do artigo abaixo que é um pouco antigo. Porém, o conteúdo é
bem atual. Este texto pode ser um material útil para cursos básicos.

Atenciosamente,
Getúlio

Link:

http://query.nytimes.com/gst/fullpage.html?res=990CE2DA1039F93BA15757C0A9679C8B63&sec=health&pagewanted=1

Trecho:

 Adding Art to the Rigor Of Statistical Science

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By DAVID LEONHARDT
Published: April 28, 2001

About a decade ago, in the mountainous Grampian region of Scotland, a team
of medical researchers began studying a drug meant to help people survive
a heart attack. Doctors would give the drug, called a clot-buster, to some
patients before they had even reached the emergency room and then compare
their survival rate with that of patients who had not taken the drug.

The results were astonishing. In a paper published in The British Medical
Journal in 1992, the researchers reported that the drug had reduced the
death rate of heart attack victims by about 50 percent. The sample size
was small -- about 300 patients -- and the Grampian region was remote,
meaning that it often took patients more than an hour to get to a
hospital. Still, the results suggested that the clot-buster might be more
effective than almost any similar drug to come before it.

A pair of British statisticians were skeptical, however. ''The amount of
data wasn't that overwhelming,'' said Stuart Pocock, one of the pair and a
professor at the London School of Hygiene and Tropical Medicine. ''So
there was a residual doubt: was it too good to be true?''

To test their doubt, Mr. Pocock and his colleague, David Spiegelhalter,
reached back two and a half centuries to a mathematical technique devised
by a Nonconformist minister named Thomas Bayes who died before his seminal
paper was ever published. Bayes's formula allows scientists to combine new
data with their prior beliefs about how the world works. It is an idea
that amounts to heresy in much of the statistical world. After all, the
method requires individuals to make subjective decisions about how
strongly to weigh prior beliefs. As a result, many scientists say it
sullies pure data with bias and outside information.