10 Responses to “Red meat & mortality & the usual bad science – Part 2”

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  1. avatar Kip Hansen says:

    All of this shows a few important things:

    1. As usual, data dredge observational studies have ONLY the ability to point to areas that might be of interest to researchers as indicators of a topic that might be subject to further research. They do not and can not ever ever ever give information about causation.

    2. Such small increases in something as vague (as far as causal factors is concerned) as ‘death rates’ despite being able to be shown to be ‘statistically significant’ are not practically significant in any real way.

    3. Hate to say it again, even if the association were HUGE (it is not, it is tiny tiny tiny at best) Association is not Causation.

    4. This study, and the hundreds of others like it, will never be scientifically sound, because of the type of study it is and the data it is based on –> it is a data dredge of self-reported diet information collected at long intervals, a study not designed to test any particular hypothesis. Given that, even if this study turns out to be statistically sound (doubtful, but possible), the ‘absolute risk’ found of death of a male increasing his red meat consumption from 3 serving a week to 2 servings a day moves from .0123 to .0130.

    5. Any study that depends on obscure statistical methods to find any result at all can safely be ignored by laypersons.

  2. Good one, Zoe. “Crude” is definitely the name of the game. Oddly, nobody questions quintiles? Why do quintiles? Because if you plotted every point (or a random sample), you would need a computer. Computers are expensive. What year is this? If you saw individual points, you would know that there is no predictive value in this stuff.

  3. avatar MS says:

    I think Zoe herself explained it best, Crowhurst, by using an example of someone singing in the bathtub. We can see clearly that singing does not cause bathing, and bathing does not cause singing!

  4. avatar Crowhurst says:

    This thread may be cold by now but if not could DT or someone else versed in statistics explain how confounding factors are “subtracted” out of a data set to reveal the relationship between the variable of interest? Intuitively, it would seem to be almost impossible to do accurately, especially if the variable of interest (i.e., meat consumption) had a relatively small association with the outcome (e.g., death) compared with some of the confounding factors (e.g., smoking, age, BMI) which have a large association with the outcome. Does it not require knowing the mathematical relationships between each confounding factor and the outcome to an unbelievable degree of precision and accuracy or else risk having these small errors appear to be a signal from the variable of interest? For example, doesn’t the analyst need to develop and input a mathematical function that relates every confounding factor, say, alcohol consumption, with mortality so that this factor may be subtracted out appropriately. It seems these relationship are known in broad terms, but not to the extreme degree of precision needed to tease out relationships between much weaker factors and outcomes. And if you get each confounding variable just a tiny bit wrong, all the little imprecisions accumulate at the variable of interest making it look like a real association when all it really is is noise.

  5. avatar DT says:

    When writing my previous comment I didn’t notice the asterisk in Zoe’s email. However, this doesn’t change the fact that looking at raw (not age-adjusted) death rates is meaningless. The paper does provide age-adjusted death rates (or more precisely, it provides age-adjusted HR), and only these number are interesting when comparing with the multivariate model numbers.

  6. avatar DT says:

    I gave answers to your questions above on the comments section of your previous blog post. As I wrote previously, your “death rate” numbers are meaningless.
    Also, if you don’t know what “age standardized” means, why do you think you are qualified to critique the statistical analysis of the study?

    The reason Dr. Hu did not reply was not because his
    research were shown to be BS. All the critiques (Zoe & Denise) have only shown that they lack knowledge of statistics.

    The link you gave does not backtrack the original findings.
    It is just an explanation of the paper to the laymen.

    The soda study shown a WEAK association between soda consumption and CHD. Thus, soda consumption cannot explain the difference in mortality between the high & low meat eaters.

  7. avatar Crowhurst says:

    A subset of the Harvard folks who published the meat study (including Dr. Hu) also just published a study using the very same data-set that purports to show a large association between soda consumption and cardiac disease and death. If there is such a large association with soda consumption, then why on earth wouldn’t the meat study have tried to correct for soda consumption. Seems it’s apparently such a large risk factor that could easily swamp out whatever signal they’re seeing from meat consumption. Is it a glaring omission, or am I missing something?

    The soda study is here:

  8. avatar MS says:

    Harvard Medical School’s HEALTHbeat newsletter (link here:)


    printed a follow up to that study today. It does seems to backtrack the original findings.

    Hope this helps.

    Keep up the GREAT blog!

    • avatar Zoë says:

      MS – Thank you SO much for this. I also notice that the article is no longer freely available – normally we would have to say “the damage has been done” but there are no so many defenders of grass grazed meat that I like to think collectively we put up a fine counter attack. I just hope the next people planning to look for a relationship between meat and blue socks, or whatever nonsense is next, now think twice!

  9. avatar Neil says:

    Wow Zoe, for a moment there I thought you must be channeling Denise Minger, an outstanding analysis.
    Unfortunately Frank was not so impressed.
    Might have been the inconvenient fact that all his fiddling with the data to provide the result he set out to achieve has been shown to be utter BS, so what does he do when called out?
    He goes and hides of course!
    Ah well, another day, another research grant. I’m just amazed that Walter Willett put his name to this rubbish. Until now I had thought he was a lone voice of reason, sad.
    Well done.

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