Flypaper for innumerates

Yet another person has tried to refute the Lancet article. John Brignell dismisses the study just because:

A relative risk of 1.5 is not acceptable as significant.

Actually the increased risk was statistically significant. You won’t find support for Brignell’s claim in any conventional statistical text or paper. To support his claim he cites a book called Sorry, wrong number!. Trouble is, that book was written by…. John Brignell. Not only that, it was drafted by… John Brignell. Brignell is a crank who dismisses the entire field of modern epidemiology as some sort of plot by scientists to scare people. We encountered him before in this post where, armed with no evidence whatsoever, he insisted that the ozone hole had always been present.

To see how silly Brignell’s “relative risk of 1.5 is not acceptable as significant” claim is, consider this: Suppose we had perfect records of every death in Iraq and there were 200,000 in the year before the invasion, and 300,000 in the year after. Then the relative risk would be 1.5 and Brignell would dismiss the increase as not significant even though in this case we have absolutely certainty that there were 100,000 extra deaths.

Comments

  1. #1 PaulP
    November 24, 2004

    dsquared: I am not confusing anything, but you seem unable to accept my simple point: that a CI refers to the entire process that produced the data that were input to calculate it, and that to go beyond that you have to consider things like systematic measurement error. It is precisely because measurement error is systematic, not randomly varying, that
    simply looking at a CI – where the CI’s inputs are the result of a measuring process – is insufficient.
    I know very well that the statement that when “the confidence interval is wide, then it is more likely that the confidence interval does not measure the uncertainty surrounding the estimate correctly” is not correct. The quoted statement is the almost the opposite of the truth, as can be seen when you think about 99% CIs being wider than 95% CIs for example.
    In case you have not got the point yet: CIs and measurement error are two unrelated things, which must be combined if, as in the case of this paper, you wish to talk about actual death rates having analysed a small sample of reports of deaths – not all verified – with questionable assumptions right at the very beginning.

  2. #2 Tim Lambert
    November 24, 2004

    This thread truly is flypaper for innumerates.

    Trichopolous’ statement is only three sentences long, but Per and PaulP seem to be incapable of understanding it, even after I explained that Trichopolous is not talking about sample size. You guys might like to try reading the last paragraph of my original post.

  3. #3 per
    November 24, 2004

    Tim Lambert
    this is the third time I have asked you to justify your completely unfounded claim that the quote I present are “taken out of context”.
    Own up to your lack of contact with reality

    Trichopoulos (allegedly); “we all take seriously small relative risks when there is a credible hypothesis in the backgeound. Nobody disputes that the prevalence of boys at birth is higher than of girls (an excess of 3%),…
    I will try to explain this slowly, since you are so slow to pick up on the point.
    Trichopoulos says that we accept low RR’s if there is a credible hypothesis. However, his example does not substantiate that theory. We accept the 3% excess of males because:
    1) we have nigh-on perfect and verified measurement of the total population of males and females born
    2) we have an extremely large sample size
    3) we have an index of the variability in these numbers
    All three of these are needed; the credible hypothesis just does not validate the measurement. In fact, it is the other way around. When you observe that there is a 3% excess, you start to develop theories to explain the fact.
    And yes, you still haven’t addressed the fact that PaulP showed you were wrong in your first post; you need to know the variability of your perfect measurements.
    you know, you are starting to accumulate a lot of damage to your credibility.
    per :-)

  4. #4 Tim Lambert
    November 24, 2004

    Per, your comment proves that the quotes were taken out of context, because you have absolutely no idea what the quotes mean. I don’t think you even understand my original post. Forget sampling error and measurment error. Brignell said nothing about them when he dismissed a RR of 1.5 as “not significant”. If he is right then a RR of 1.5 can be dismissed even if there is no sampling error or measurement error. No epidemiologist or statistician would agree with Brignell.

  5. #5 per
    November 24, 2004

    “24/11/2004 05:05:29 Scott and Ken are quite right about the quotes Per presents as being taken out of context.”
    I have the science article in front of me. That is an outright falsification, and you have had three opportunities to retract. You have deliberately told an untruth, with intent. You are a liar.

    “No epidemiologist or statistician would agree with Brignell.”
    you ignore quotes from Agnell, Temple, and a whole host of other prominent epidemiologists, who all make exactly the same point as Brignell in the science article- including Sir Richard Doll. This is another deliberate falsification, and what is more, you deliberately write this when you know this to be untrue.
    you are quite prepared to fabricate and tell untruths. You are not a scientist.

    by the way, you have set up a straw man argument. No-one argues that an RR of 1.03 cannot be significant- if you have a properly controlled experiment with adequate design. The issue is that the vast majority of epi studies are not so controlled.
    per

  6. #6 Tim Lambert
    November 24, 2004

    Per, the quotes are taken out of context and you have no clue what the epidemiologists are talking about. The RR of 1.03 that Trichopolous mentions is not from an experiment but observations.

    This is my blog, I will not put up with you calling me a liar. You are no longer permitted to post here.

  7. #7 A non
    November 24, 2004

    If the quotes are taken out of context, put them up on the web for everyone to see. You won’t, because you can’t. You are a liar.
    I don’t have a clue what epidemiologists are talking about ? That would be ad hominem abuse then ?
    Hey Tim,
    if you want to rave on in your blog, and don’t want to be bothered by any of these “facts”, it’s all yours. I won’t darken your doors again.

    thing is- now- you will always be a liar.

  8. #8 Scott Church
    November 25, 2004

    Thank you Tim! Chalk up one more for reason and statistical literacy.

  9. #9 Dano
    November 25, 2004

    Get that man a screwdriver for some loose cranial screws.

    I believe I’ve said this before, but it bears repeating: Tim, you have an amazing capacity for patience.

    Best,

    D

  10. #10 PaulP
    November 25, 2004

    Tim:
    I think you are confusing what I am saying with the comments of other people.
    To be clear: the Trichopolous quote indeed does not mention sample size but the 3 examples cited do not involve any sample at all – each case involves an analysis of a completely counted set. To do the same this paper would have had to analyse a total count of (actual) deaths in all of Iraq for the period in question.
    To turn this on its head: we cannot elide the distinction between these two situations. The unquestionable strength of the three cases comes from factors such as that the total population is individually known (not even just by statistical aggregate such as mean or variance), that its geographical distribution is individually known (where relevant), and that no mathematical adjustments need to be made to the raw numbers to filter out the effects of unwanted influences (such as confounding factors). But these are the very things absent in papers such as these, so that whatever we can say about situations such as the 3 cases cannot automatically be said for papers like this one.
    (And can you accept that however much frustration we may both feel, there is no animosity or ill-will on my side?)

  11. #11 John
    December 23, 2004

    Tim Lambert:

    This will probably not be read so late in the game, but I will send it anyway.

    Thanks for a great discussion. I am an old retired f..t that happened upon this thread I know not how. I know very little about statistics but reading this thread was a treat and enriching for someone that has no opportunity for academic interchange. I live in a small town where dial-up internet access is the state of the art. Sad, really. The last contact I had with the actual study of statistics was in undergrad many decades ago, so this thread was a fun education for me.

    There was a contributor, Scott Church, who seemed to be respected by most on the thread. In his 22/11/2004 19:12:01 comment, Scott stated in a larger comment, “And Over here…. we’re treated to his glowing review of a book titled “The Cholesterol Myths” by one Uffe Ravnskov in which we’re told that saturated fats and high cholesterol pose no risk whatsoever to human health.” Since this was entered during the discussion on credibility, I hope Scott will take the time to actually read the book he referenced. Neither the book nor the website he referenced actually say what Scott says they do. If one wishes to address the credibility issue, then credibility is an issue. Based on what I read elsewhere on the thread, I feel confident Scott would enjoy reading the book. It has been a few years since I read “The Cholesterol Myths” but it seems as if the first topic discussed with its review of the literature and the result of the review (Not summaries but actually reviewing the studies with their “conclusions”) would make a credible person grind their teeth. A person steeped in statistics would possibly go ballistic. The literature reviewed in the book is the “credible” literature.

    If you have contact with Scott, please send along my recommendation. I enjoyed reading his comments up to the point he talked about something I actually knew something about and realized this particular comment was not credible. I was disappointed since it, perhaps unfairly, caused me to look at his later comments with less respect. Based on his comments, Scott probably deserves better than that, thus I hope he reads the book.

    Thanks again.

  12. #12 Scott Church
    December 27, 2004

    John, Thanks for the constructive criticism. It is true that I didn’t pursue Dr. Ravnskov’s book in depth as it was off the subject being discussed at this post. My comments were made rather hastily in regards to the glowing praise he received at one of the sites I found that was pushing Brignell’s book “Sorry, Wrong Number” – which contains many of the poor statistical arguments used by to improperly denigrate the Roberts et al. Lancet study which we have been discussing here. As you know by now I’m sure, Brignell publishes much crank work. But given that medicine is not my strong point, I could have made a better case in this regard by either considering another book at the site in question, or by examining Dr. Ravnskov’s work more closely.

    That having been said, I’ve tried looking some more at his claims. His web site (at least, what I’ve seen so far of it) makes reference to his “myths” by contrasting them with his statements of fact. As I read over this list, I see many things that seem indisputable, but straw men as he presents them in regards to the merits/demerits of Cholesterol (Not that I wouldn’t LOVE to believe the man, mind you. MY LDL cholesterol could be a lot lower :) ). The one statement I see that strikes me as questionable is his remark that “There is no evidence that too much animal fat and cholesterol in the diet promotes atherosclerosis or heart attacks.” While he does cite sources for this, I haven’t had the time to review them yet. But beyond this, I am concerned by the fact that so far I am unable to find much support for his statements in the general literature. For a claim as dramatic as this, I would expect much more from the refereed literature than what I’ve found. So far, my searches are turning up only popular amateur health pages, rant sites of various sorts, and Far-Right sites. I’ve not yet come up with much from Medline, the National Institute of Health, JAMA, the New England Journal of Medicine, and the like (though I see he has published in these journals on different subject, or at least been cited therein). Meanwhile, The National Academy of Sciences (2002) has published a report on dietary health and dietary guidelines titled Dietary Reference Intakes for Energy, Carbohydrate, Fiber, Fat, Fatty Acids, Cholesterol, Protein, and Amino Acids (Macronutrients). Chapter 9 discusses cholesterol at length, and while it seems to support some of Dr. Ravnskov’s more general claims, it does not appear to me to support his thesis that high levels of dietary animal fat and cholesterol do not promote atherosclerosis or heart attacks. I’ll grant you that my own jury still needs to be out until I can demonstrate my concerns regarding Dr. Ravnskov better. But what I have found so far does not look very promising for him. I’ll see what another few days turns up.

    In the meantime, I trust you support my basic claims, and Tim’s, regarding Brignell and many of the statistically questionable arguments levelled at the Roberts et al. Lancet study on the Iraq War. If not, pass along the contrary data and I’ll stick my nose back in the books.

    Thanks again for the constructive criticism. Iron truly does sharpen iron, and I need to have my own edges honed regularly just as much as the next guy! Best.

  13. #13 Tribbs
    December 27, 2004

    (Now that this thread has been revived:)

    Three-part New Year’s Quiz:

    How many Iraqi civilians were violently killed during (not subsequent to) the March – April 2003 invasion of Iraq?

    (a) “Significantly” more than 3,240, according to whom, based on what?

    (b) “Up to” 7,350, according to whom, based on what?

    (c) “About” 15,500, according to whom, based on what?

    Bonus question: Which of these has the widest margin of error, and why?

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