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From: freelee love (124.184.17.25)
Subject:         A real epidemiologist speaks up on where Denise went wrong
Date: July 11, 2010 at 12:49 am PST

In Reply to: The China Study raw data yet again misinterpreted posted by Freelee Love on July 10, 2010 at 11:38 pm:

This just in from a lady in the know...

OH MY. By request of beautiful Freelee, I've taken a look at Denise's
analysis. I'm an epidemiologist, and on top of that my research
focuses on cancer (not that this makes me completely infallible, but
at least I feel equipped to provide an informed critique of her
statistical capability). Dr. Campbell was certainly gracious in his
response to criticism, but I cannot be so kind. Denise is incredibly
naive in her crude analysis of the raw data. She uses correlations
and ecologic comparisons to draw conclusions about relationships
between diet and outcome (cancer, cardiovascular disease, etc.).
WRONG WRONG WRONG!!

A correlation does not an association make.

And, as epidemiologists, our studies are intended to determine
associations between exposures and disease. (Yes, there are special
methods to determine actual causes of disease, but for most of us,
associations will do.)

Let me explain in a nutshell - a correlation is a linear (assumes a
"straight-line" relationship - but not all things are related in this
manner), unadjusted (does not account for multiple factors that
could potentially confound the relationship between an exposure,
like diet, and outcome, like cancer), and non-directional (it does not
say if one caused the other or the other way around). An
association, on the other hand, is generally adjusted for potential
confounding factors and - if a study is properly conducted - gives
us an idea of temporality or direction.

Denise, while meticulous, went through a series of exercises only
to:

1) Provide a series of correlations, which honestly, is just the FIRST
STEP of any good statistical analysis. Typically more complex
modeling of the data ensues so that multiple factors can be
accounted for when investigating the relationship between an
exposure and the outcome.

2) Much of her conclusions are drawn from purely ecologic data -
that is, data that is in aggregate - such as comparing the average
fat consumption in Japan and the U.S. and the corresponding
breast cancer rates. Sure, it can be informative, but it doesn't tell us
anything about some of the other factors that might be related to
fat consumption and breast cancer. Ecologic studies are considered
to be at the bottom of the "epidemiologic study totem pole." And
we can NOT draw individual-level conclusions from them, i.e. we
cannot say that an individual who consumes less fat will, on
average, be protected from breast cancer (even if that's true, we
cannot draw this conclusion from an ecologic study - there's even a
term for it: "ecologic fallacy").

OK, my disclaimer: I'm an epidemiologist, and yes, scientists are
NOT objective (I'll say it: I'm an ardent veggie with a happy veggie
family). Hell, science is not objective. I mean, you could be given a
blob of numbers that mean nothing. It takes some context,
interpretation, and data processing to make anything meaningful
out of those numbers. Yes, scientists can be biased, and so can the
studies they conduct, and the analysis of those studies. But good
scientists do the best they can, are open about their methods, and
fair when discussing their results. I applaud Dr. Campbell for
making his raw data available - very few scientists do this. I will be
totally honest and say I have not read "The China Study" (I guess I
feel it'd just be preaching to the choir, but I think I will read it
now...). But I know enough to know that Denise's analysis was
crude at best and completely wrong at worst. No card-carrying
epidemiologist would EVER be able to publish her results and draw
the conclusions that she does.

I've posted the following comment on Denise's blog (which, was
there for a few minutes, and now when I go back to the site, it is
mysteriously not there anymore...):
Your analysis is completely OVER-SIMPLIFIED. Every good
epidemiologist/statistician will tell you that a correlation does NOT
equal an association. By running a series of correlations, you’ve
merely pointed out linear, non-directional, and unadjusted
relationships between two factors. I suggest you pick up a basic
biostatistics book, download a free copy of “R” (an open-source
statistical software program), and learn how to analyze data
properly. I’m a PhD cancer epidemiologist, and would be happy to
help you do this properly. While I’m impressed by your crude, and –
at best – preliminary analyses, it is quite irresponsible of you to
draw conclusions based on these results alone. At the very least,
you need to model the data using regression analyses so that you
can account for multiple factors at one time.

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