Saturday, September 29, 2012

Causation and Statistics - a lesson from Sir Austin Bradford Hill

Sir Austin Bradford Hill (1897-1991) and Richard Doll were the first scientists to establish the causation of cancer by cigarette smoking. Even though Hill pioneered the randomized clinical trial, he recognized that there were many circumstances in clinical research were the RCT could not be used.

He was particularly interested in proving causation; he developed nine criteria and used them to convince the scientific community that smoking caused cancer. Here are the Hill Criteria:

(1) the strength of the association
(2) the consistency of the observed association
(3) the specificity of the association
(4) the temporal relationship of the association
(5) the biological gradient (dose/response relationship)
(6) the biological plausibility – the mechanism by which local anesthetics cause chondrolysis is consistent with the current understanding of mechanisms of cytotoxicity;
(7) coherence - does the cause-and-effect interpretation of the data conflict with the generally known facts of the natural history and biology?
(8) experimental evidence
(9) analogy 

You may be interested in his writing "The Environment and Disease: Associationor Causation?"

He concludes this article with the following 'poke' at those of us who excessively emphasize "p<":

“I wonder whether the pendulum has not swung too far – not only with the attentive pupils but even with the statisticians themselves. To decline to draw conclusions without standard errors can surely be just as silly? Fortunately I believe we have not yet gone so far as our friends in the USA where, I am told, some editors of journals will return an article because tests of significance have not been applied. Yet there are innumerable situations in which they are totally unnecessary – because the difference is grotesquely obvious, because it is negligible, or because, whether it be formally significant or not, it is too small to be of any practical importance. What is worse the glitter of the t table diverts attention from the inadequacies of the fare. Only a tithe, and an unknown tithe, of the factory personnel volunteer for some procedure or interview, 20% of patients treated in some particular way are lost to sight, 30% of a randomly-drawn sample are never contracted. The sample may, indeed, be akin to that of the man who, according to Swift, ‘had a mind to sell his house and carried a piece of brick in his pocket, which he showed as a pattern to encourage purchasers.’ The writer, the editor and the reader are unmoved. The magic formulae are there.
Of course I exaggerate. Yet too often I suspect we waste a deal of time, we grasp the shadow and lose the substance, we weaken our capacity to interpret the data and to take reasonable decisions whatever the value of P. And far too often we deduce ‘no difference’ from ‘no significant difference.’ Like fire, the chi-squared test is an excellent servant and a bad master.”

Makes one think.....


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