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I have followed with great interest the interchange of emails over last weekend, involving Steve Simon, Brian Budenholzer, Kevork Hopayian, Klasien Matter-Walstra and Michael Bigby. Maybe I excessively censored my straw poll, possibly incurring selection bias in the process, but it appears that 2 out of 2 respondents who expressed a preference (BB & MB) preferred the singularity of the NNT as the method of expressing a treatment's possible inefficacy! So be it. Even away from the singularity, the ARR and NNT, though they contain equivalent information, look rather different mathematically. I wouldn't advocate bandying about small decimals like 0.00235, but 2.35 per 1000 seems assimilable. A fair degree of rounding is sensible, when professionals talk about study results informally among themselves, but the definitive documentation should give things to "about two-and-a-half" significant figures, whichever scale is used. (I can't give a reference for this quote, I'm afraid.) KH and KM-W extend the discussion to communication with patients. KH asks whether there is evidence that NNT is superior to ARR in this respect. First, I'd point out that on several occasions I've seen reference made to evidence that professionals can form quite different interpretations of the same data, presented in different ways - though I haven't the reference to hand - I guess that it follows that patients would do the same. But the issue goes a lot deeper than this. The British education system, at least, has always failed to instil basic statistical numeracy into the population. The most persuasive evidence for this is the tremendous success of the national lottery etc. And from what I've seen, similar organisations in other countries have no problem maintaining profitability. The national curriculum is going some way to redress the balance, but it isn't easily achieved. The upshot of all this is, not whether Joe or Jo Public can understand an NNT of 9 better than an ARR of 11%, but just what either of these expressions of difference in risk really means to them. With a naturally totally selfish outlook here, as pointed out by KM-W, your being the 1 in 9 doesn't disadvantage the other patients in the queue as they're all independent, so "you're the only one that matters" is unequivocally right. (Though they often can't see this, and invoke the "law of averages".) And with a (generally) very fatalistic, external locus of control - again, evidenced by the success of the lottery - which is often why they persist in the lifestyle that leads to "caseness". Sorry if this sounds very right-wing - I believe fully in this half of the inverse care law, as well as in the left-wing aspect, what Julian Tudor Hart meant in terms of provision being inversely related to need - and which is invariant under changes of colour of government which may be merely cosmetic. Furthermore, I'd question whether the ARR vs. NNT issue as we've discussed it really carries across in the same way to discussions with patients. I've been very scathing about the NNT, from the standpoint that I'm a statistician and it is my professional role to communicate with clinicians about how evidence should be expressed, *together with an appropriate expression of sampling imprecision*. I realise that many of those using these measures are clinicians, who are in the *very different* business of explaining to patients and relatives. In this situation, there does not appear to be a place for putting a quantitative expression of uncertainty around the estimated ARR or NNT - if the evidence is very poor, I guess you just say it's very poor. Also, even if you are basing your advice to the patient on a meta-analysis rather than a single study, you probably won't want to place too much emphasis on the exact ARR or NNT that was calculated, but you'll tend to modify it on an ad-hoc basis, either upwards or downwards, in the light of the great deal of information you already know about the patient - even if only history, age and gender. Traditionally, the reporting of calculated numerical risks to patients has been the preserve of medical geneticists, where it is of course appropriate. I think that in other contexts, notwithstanding the availability of the evidence base, we should give careful thought to whether it is really appropriate to do so. Robert Newcombe. .......................................... Robert G. Newcombe, PhD, CStat, Hon MFPHM Senior Lecturer in Medical Statistics University of Wales College of Medicine Heath Park Cardiff CF14 4XN, UK. Phone 01222 742329 or 742311 Fax 01222 743664 Email [log in to unmask] Macros for good methods for confidence intervals for proportions and their differences available at http://www.uwcm.ac.uk/uwcm/ms/Robert.html %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%