-----Original Message-----
From: Evidence based health (EBH) [mailto:
[log in to unmask]] On Behalf Of Stephen Senn
Sent: Monday, November 08, 2010 6:30 AM
To:
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Subject: Re: SBM-EBM shouting match
Richard Royall is a statistician who has had much to say about evidence. He points out that on having completed a study there are three rather different questions we might wish to answer.
1. What do I now believe?
2. What should I now do?
3. What is the evidence of this study?
Bayesians are concerned with 1 and (to the extent that they consider utilities also) with 2. These are good and laudable concerns.
However, it as an irony of Bayesian analysis that naive Bayesians are wont to overlook that as the late George Barnard, the father of the likelihood principle, pointed out, Bayesians cannot use a set of posterior distributions from various studies as the raw input to a Bayesian analysis. They can often make better use of standard frequentist summary statistics (although P-values are very problematic and stopping rules are not really relevant).
So personally, I think that there is room within the statistical menagerie for people who want to do EBM and those who want to do SBM without their having to snipe at each other.
Stephen
-----Original Message-----
From: Evidence based health (EBH) [mailto:
[log in to unmask]] On Behalf Of Croft Daniel (RBF) NOC
Sent: 08 November 2010 10:54
To:
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Subject: Re: SBM-EBM shouting match
Dear List,
Would someone mind translating this debate into layman language for people like me who are new to statistics?
Am I right in thinking that the people who argue for using Bayesian statistical methods want to consider what we know already when analysing new data. They do this by estimating values to represent that prior knowledge that then affect the analysis of the new data.
The non-Bayesian people consider each new test with no prior considerations, so that any result is possible.
And do these two camps generally split into Science-Based Medicine people and Evidence-Based Medicine people respectively?
Regards,
Daniel
-----Original Message-----
From: Evidence based health (EBH) [mailto:
[log in to unmask]] On Behalf Of Michael Power
Sent: 08 November 2010 10:25
To:
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Subject: SBM-EBM shouting match
David Gorski at the Science-Based Medicine blog has posted a put-down of Steve Simon's defence of EBM:
http://www.sciencebasedmedicine.org/?p=8151
I won't join this shouting match, or comment on the gratuitous ad hominems, or remark on variation of the "but Brutus is an honourable man" rhetoric, or draw attention (again) to the reification of EBM into a fantastic strawman.
But, I do want to ask a serious question, provoked by Gorksi's comment that EBM ignores prior probabilities.
There must be a reason why Bayesian methods are so seldom used. Professor Sir Michael Rawlins suggests five possibilities in his much lauded Harveian Oration (
http://bookshop.rcplondon.ac.uk/contents/pub262-9bc950aa-00e6-4266-8e80-e4bc63a25262.pdf):
1) People "prefer the apparent (but illusory) security of a clear definition of what constitutes an 'extreme' result when tested against the null hypothesis, and they are reluctant to accept that either personal belief or judgement should come into play in decision making." "clinical investigators are
much more reluctant to accept a subjective approach to the interpretation of probability than statisticians."
2) "there have been substantial controversies about the derivation of the prior probability" "too much has been made of the alleged difficulties"
3) "Bayesian analyses are computationally complex"
4) "some statisticians - albeit a dwindling number - are unfamiliar with the techniques of Bayesian analysis and are unwilling (or unable) to adapt. Some generously attribute this variation in skill-mix to a statistician's original choice of university. Others, less kindly, believe it to be generational. As one Bayesian explained to me: 'Statisticians who were taught how to use log books and slide rules can't usually do Bayesian statistics'"
5) "regulatory authorities have sometimes been hesitant to concede that Bayesian approaches may have advantages"
These reasons, especially the 4th (I was taught a long time ago to use log books and slide rules), seem to me to be insufficient to explain why Bayesian methods are not more widely used.
The question I want to ask is (at the risk of exposing my naiveté wrt Bayesian methods): do the proponents of Bayesian methods mistakenly assume that p-values and 95% confidence intervals indicate the true probability and true range of measurements?
Well trained EBMers know that p-values and 95% CI-s are indicators of precision (statistical variation). P-values and 95% CI-s are not indicators of accuracy (truth). This requires other information, which is provided by critical appraisal and "triangulation" with other results.
The way I understand it (or misunderstand it) is that with Bayesian methods the subjective assessment is done when the prior probability is chosen (note, it is not measured, even if a measurement is chosen). But, with frequentist methods, the subjective assessment is done when the risk of bias and error is assessed with critical appraisal. In other words, both methods require qualitative assessment of their results.
I prefer the frequentist method because it makes transparent the essential role of critical appraisal to assess accuracy (risks of bias and error). Am I wrong?
