The Frugivore Diet

denise minger 'apologizes'

2015-10-07T04:15:41.000Z

well sort of anyway.

this was sent by bryne carruthers who did the

Has Denise Minger Read 'The China Study'?

here is the text, an apology from Denise to the “Plant-Based Diet Doctor Squad” (Caldwell Esselstyn, John McDougall, Dean Ornish, Neal Barnard, and by some definitions Joel Fuhrman):
For the past few years (five? six? seven ate nine?), I’ve asserted that the success of plant-based diets is due to their whole-foodsness (eliminating processed junk, refined sugar, and refined flour), their low PUFA intake regardless of total fat (with the implication that higher non-PUFA fat consumption would be hunky dory), and the increase in other health-promoting behaviors that come with making a big change in the foods you eat (more exercise, less drinking and smoking, less couch-potatoism, etc.). I still think those things are relevant. But I now believe I dismissed the role of low total fat intake before I gave the data a fair and thorough analysis. This is a breach of the standards I hold myself to as a science blogger, which involve impartially examining all evidence before drawing conclusions.

So, I’d like to take this opportunity to say, HEY GUYS: I’m sorry.

To the aforementioned doctors: I’m sorry for jumping the gun with your research and being snarky when I reference you. I don’t always agree with the way you interpret your own work; I’m often unsettled by your debate tactics; I worry that the co-mingling of animal rights activism and nutritional research is toxic for remaining scientifically objective. But good heavens, I sure dismissed y’alls results before digging as deep as I should have—and in the process, missed out on some amazing opportunities to broaden my understanding of diet (and communicate those findings to my readers). Thanks for being on this planet and helping broken people heal.

http://rawfoodsos.com/2015/10/06/in-defense-of-low-fat-a-call-for-some-evolution-of-thought-part-1/#modern

better late than never? possibly, had she appreciated bryne's rebuttal above from 2010, it would have made things simpler.

it doesn't really matter though. :D

in friendship,

prad

A new critique addressing the serious inaccuracies and flaws in the claims of Denise Minger and the cholesterol skeptics

2012-08-21T13:47:43.000Z

I have created a couple of posts addressing the very serious inaccuracies and flaws in the claims of Denise Minger and the cholesterol skeptics, and have included several hundred references with easily accessible links.

http://healthylongevity.blogspot.com/2012/08/forks-over-knives-and-healthy-longevity.html

I have already posted this on the forum under ‘The Science’ board, but thought it may also belong here as it also addresses the China Study. Let me know if you have any thoughts on the posts.

helping others to pick up the book

2012-06-22T12:15:21.000Z

howdy guys!

i have chatted to my dad about this book and recommended it....he has gone as far as buying it but i am hoping that when he gets around to reading it it will inspire him to change his eating habits! It honestly saddens me to think of how little thought or idea he has of the junk he is putting in his body! He is a sugarholic! I dont want to be one of "those" people who pushes their own beliefs on other people....

Has any one personally experienced or know of someone who has read this book and changed their thinking? I just want my dad to feel as amazing as i do and i know he wouldnt look back if he knew what he was missing!

Any feedback is welcomed! thanks guys! :)

Criticisms of China Study & Veganism on Redneck Blog...

2011-11-04T17:55:38.000Z

Hey guys! I was having a discussion on a blog of a guy named Elliot Hulse (who has quite a following, btw). He promotes a high protein/fat, animal product diet (Weston Price, Paleo/Primal, redneck, etc...).

http://www.youtube.com/watch?v=JFjdBNn6n2M&feature=channel_video_title http://www.youtube.com/watch?v=C7e_86OvrfQ&feature=channel_video_title ("EAT MEAT" LOL!)

The discussion got a lot more in-depth than I intended, but I found it very stimulating nonetheless. I was going to copy and paste the blog entries, but I figured it would just be better to provide the link. Here it is: http://www.hulsestrength.com/build-crazy-muscle-vegetarian-diet/comment-page-1/#comment-49693 (just skip down to the bottom, under Rob - first post on Oct. 29)

Let me know what you guys think, and/or if you'd like me to copy/paste it on the discussion page! =)

L&R

~Rob

I need some info

2011-06-13T07:46:14.000Z

Hey guys, please let me know; does anyone have a list of other studies that have similar findings to the China Study proving the vegan diet is healthiest?

b-d debate for dummies

2010-11-08T06:56:52.000Z

The B-D Debate For Dummies

at long last, the work no one was waiting for, is finally here!
just don't tell rex and audrey, ok?

in friendship,
prad

Scenario

Have you been baffled by what's been going on regarding the China Study discussions over the summer of 2010? Has Denise Minger's long and detailed postings stressed you out? To make matters worse, there is this new kid on the block B, who writes with equal analytic fervor and is challenging the content of Denise's enormous work in detail!!

Do you have trouble keeping up? Do you get restless as the arguments get nested deeper and deeper? Do you look at a word like schist.., schistoso.., um .. schistoso ..so ..so what!! and lose your composure? Do you just stop reading after two paragraphs and post some sort of adulatory comment in the hopes of making people think you actually know what is being discussed?

Well worry no more because this document is for you! Through it you will be able to follow and understand the essential points of the argument. It is written with sufficient simplicity, enough explanations and opulent orientation so that anyone can 'get it'! In our humble opinion, it is very important to 'get it', because this discussion focuses on health - your health and you should not be a passive bystander.

We have taken into consideration the educational background of the protagonists. B was heading to do graduate work in musicology and Denise majored in English. So what do you get when you put English and music together? You get a musical!

This musical will draw upon Lerner and Lowe's My Fair Lady. However, to avoid plagiarism charges our title will be My Fair Malady. The role of Eliza Doolittle will be substituted for by Deliza, while Professor Higgins will appear as Professor Biggins. (I do the best I can with the limited budget and creativity I have, ok?)

We have uploaded the screenplay and here is the official poster:

So on with the show!

malady.pdf

campbell coalition site material

2010-09-10T19:30:33.000Z

the campbellcoalition.org site is no longer existent due to restrategizing efforts. some of you never got around to seeing some of the excellent items that were on that site, so nelson campbell has kindly offered to make these available for posting within our group.

you can find them easily through the toc below.

in friendship,
prad

8 blog entries by nelson campbell:
An Introduction
Really So Bad
Nature vs. Nurture; Psychopaths and Veggies
Are We Crazy? Perhaps, but Hopefully Not
Telling the Truth
Improving Our Website
A Pile of 30 Papers – Just to Play a Sport?
My Walk in the Woods

6 blog entries by dr. t. colin campbell:
An Introduction
Genes and Big Pharma
Breaking Our Addiction to Drugs
How Science Misleads
Are All Proteins Created Equal?
Filling a Hole in Government

an excellent article by dr. t. colin campbell:
A Primer on Statistics
particularly worth reading since it outlines some very key concepts in good scientific process

china study reading group

2010-08-11T18:24:52.000Z

for anyone interested in reading and discussing the china study - this is the place!
it can be done in a scheduled fashion or just randomly.
in any case, this thread should likely be of use to people who haven't read the book.

in friendship,
prad

bananalysis

2010-07-24T04:13:48.000Z

welcome to bananalysis!

if you have forgotten why you are here, read the original post in this thread:
http://www.30bananasaday.com/group/debunkingthechinastudycritics/forum/topics/bananas-stats-course

otherwise, here's what you can expect from this course:

1. learn enough statistics and study design to hold your own in conversation with anyone who throws numbers at you.

2. have the ability to conduct your own analyses so you don't have to take anyone else's word that this is what the data shows.

3. develop sufficient skills to acquire a technical grasp of the china study and other published items which use statistical analysis to make their point.

the primary course materials are the following

1. the very well done carnegie-mellon open learning initiative statistics course (henceforth referred to as cmolis not to be confused with cialis):
http://oli.web.cmu.edu/openlearning/forstudents/freecourses/statistics
this course is interactive, well-illustrated and even has video explanations.

2. supplementary exercises using actual data from the china study.
the bonus here is that you become familiar with this data by working on it directly.

3. statistical software
we recommend R because it is open source, gnu and excellent! you can learn how to get started with it by going here:
http://wiki.math.yorku.ca/index.php/R:_Getting_started
(but ask if you have any difficulties or have no clue what to do)
alternately, you can use excel if you have forked out the money or pirated a copy and feel you must use it - the course does offer an excel stream as well.
if you have some other means of doing stats (eg scipy with python, spss or an abacus), there is nothing stopping you from using it, though you will have to obviously modify specific instructions to your software.

the routine will be as follows:

1. the module to be covered will be added under "bearings" beneath existing items in bold (see below).

2. you are to do the topic from cmolis and post any questions or interesting points of observation you may have in this thread where they will be addressed.

3. extra exercises using china study data will be presented for discussion and completion.

your course guides are:

1. normal though occasionally skewed

2. inviting of others to contribute and assist just for some variance

official start day is monday, july 26, 2010!

till then, you can still do some things ... see bearings below.

in friendship,
prad

======
bearings
======

get your software in place - ask for help if you have trouble
then work on what is below. note that bolded items show
the topic presently under discussion.

unit 1: Introduction
    assignment #1
        a solution in R
        a solution in excel

unit 2: Exploratory Data Analysis (EDA)
    Introduction
        assignment #2a
    module 1: Examining Distributions

resources

2010-07-20T21:32:25.000Z

this is the place to gather together various resources that are useful to the group.
items would include data links, actual data, published papers of specific interest, analytical efforts, links to 'resource oriented' websites.

the idea of this thread is to provide easy and quick access to materials to everyone as well as show the overview of what is out there which relevantly relates to the china study and the china project.

(it should not be confused with Repository for relevant publications thread which is specifically for peer reviewed articles relating to animal protein consumption and health)

in friendship,
prad

Colin Campbell eCornell three course online education program

2010-07-20T08:12:03.000Z

There is an option to begin the Colin Campbell eCornellthree course onlineeducation program for Plant-Based Nutrition tomorrow. If anyone else is taking the courses and would like to exchange ideas or prepare for assignments please respond to this thread. Perhaps we could create a list of members here, who are taking the courses.

I read The China Study but gave the book away as a present. I don't think it is a requirement for taking the courses but perhaps it would help to have it as a reference.

Chris

Blog Bandits

2010-07-15T23:58:33.000Z

A fun name for a serious group :)

Ok who is keen to join me in spreading important information in regards to the TCS? It won't take very much time out of your day and will be very rewarding. Rewarding in the sense that if you look at the bigger picture your work is potentially saving the lives of many many people and animals (due to the merits of TCS and a vegan diet staying intact)

We can get our heads together and use this space to share idea's on formulating appropriate future responses - that can then be distributed on certain blogs by yours truly and anyone else who can assist :)

So first line of focus....

We need to formulate a fantastic response that can be circulated throughout the blogs listing Denise's critique, a lot may not be accepted but I've found a lot will be as well. The ones that do not feature responses from us are obviously HIGHLY threatened by our message, I have a certain level of respect for the blogs that do take the time to address comments against Denise's claims.

OK main defence Denise and other followers of her work are using is....

* Campbell did not perform his analysis on raw data however Campbell's correlations all perfectly match the raw data...Denise says she's "simply replicating the methods Campbell used "

(FROM B: How they used the exact same methods and came to completely opposite conclusions is beyond my small brain lol.)

* Denise claims she used the improper data, but now she's claiming campbell did as well. (on this from Vegamama : it's becoming apparent that she is confusing the raw data with uncorrected data.)

From Dr Campbell's 2006 Rebuttal....

In contrast, the critics are uncritically using highly selected detailed observations with no respect for context. They rely rather heavily, for example, on choosing selected but uncorrected correlations (associations of one variable with another) from the huge number (about 100,000 or so) that were published in the 894-page China Project monograph itself (Chen, J., Campbell, T. C., Li, J., and Peto, R. Diet, life-style and mortality in China.

(From B- It seems as though Denise is doing the same on her blog.)

Here is a list of the majority of blogs featuring Denise's claims...

http://freetheanimal.com/2010/07/the-china-study-smackdown-roundup.html

The China Study exposed: actual data does not support vegetarian health claims (at Hunter-Gatherer)
China Study Problems of Interpretation (at Whole Health Source)
Polish a turd and find a diamond? (at PaNu)
The China Study: Junk Science and Lies (at Robb Wolf)
There is no justification for a plant-only diet (at Conditioning Research)
Rest in peace, China Study (at The Healthy Skeptic)
“The China Study”, Debunked (at Theory to Practice)
"The China Study: Fact or Fallacy?" (at Let Them Eat Meat)
Destroying China (the Study that Is) (at Aspire Natural Health)
The China Study Discredited (at Food Renegade)
The Study Everyone Talks About: Part 2: The Ravaging Reviews (at Feasting on Fitness)
Debunking The China Study (at Crossfit 1776)
The Debunking of the China Study (at TJ's Gym)
Thoughts on Friday from the middle of the road! (at A Moderate Life)
A Critique Worth Reading (at For His Glory & for Our Good)
"T. Colin Campbell’s The China Study: Finally, Exhaustively Discredited" (at Crossfit Peachtree)
The China Study: Crushed by its Own Data (at The Spark of Reason)
China Study & T. Colin Campbell: Someone just made you their vegan bitch (at Paleo-ish)
The China Study: Evidence for the Perfect Health Diet (at Perfect Health Diet)
The China Study Has No Clothes: Smackdown Of T. Colin Campbell (at Nutrition and Physical Regeneration)
The slam-dunking of "The China Study" (at the shmaltz)
China Study Shakedown (at Natural Messiah)
The China Study Toppled – A Tale of the Confirmation Bias (at Lean, Mean, Virile Machine)
Slaying of a Hypothesis (at Animal Pharm)
"Epidemiology is Bogus" (at Evolutionary Psychiatry)
China Study Unveiled -- Not Supporting Veganism (at Primal Wisdom)
China fiction? (at The Heart Scan Blog)
The China Study - A Superb Analysis (at Primal Muse)
Chipping Away at the China Study (at Liberation Wellness)
The China study: Debunked (at Food, flora and felines)
Buh-bye, China Study (at The Low-Carb Curmudgeon)
China Study Debunked (at The Red Pill)
Around the Fitness Horn (at x lyssa)
RAW FOOD SOS sobre o China Study (at Canibais e Reis)
Die veblüffende Biegsamkeit von Fakten: The China Study (at Urgeschmack)
Veganbibelns fall (at Kostdoktorn.Se)
Weekend Link Love (at Mark's Daily Apple)
The China Study (at Kat's Food Blog)
Debunking junk science: goodbye china study (at abundant brain & health)

Official responses to the critics - What we have so far...

2010-07-15T23:08:14.000Z

I felt this thread was needed, just to keep all our important responses in one easy-to-find location.

Repository for relevant publications

2010-07-14T22:34:57.000Z

I'd like to start this thread where we can keep a list of published articles related to diet. Please also list citations that critics of Campbell have included in their arguments. My plan is to obtain the articles, read them, summarize, and (hopefully) offer meaningful commentary on the studies strengths and weaknesses.

Summaries of the other China Study critiques - volunteers needed

2010-07-14T17:09:01.000Z

Ok so here are all the major China Study critiques on the web I'm aware of, see below.

We need some volunteers to do concise summaries of the argument and content of these critiques.

We're looking for commonalities in subject matter (there are plenty as you'll see) so we can address them all (along with Denise's) in one comprehensive rebuttal.

Let me know if you find any others. Thanks folks.

**Format for summaries:

Title of critique:
Thesis:
Summary:
(summary should be done on a point-by-point basis for easy reference)

Chris Masterjohn

"The Truth about the China Study" (2005)
http://www.cholesterol-and-health.com/China-Study.html

"Response to T. Colin Campbell" (2007)
http://www.cholesterol-and-health.com/Campbell-Masterjohn.html

"Denise Minger Refutes the China Study Once and For All" (2010)
http://www.westonaprice.org/blogs/denise-minger-refutes-the-china-study-once-and-for-all.html
(Masterjohn's fawning review of Denise's latest critique)

Anthony Colpo

(originally posted under pseudonym 'JayY')

"The China Study: More Vegan Nonsense!" (2006)
http://www.anthonycolpo.com/the_china_study.html

Dr. Mercola

"Why the China Study is Flawed"
http://articles.mercola.com/sites/articles/archive/2006/02/23/why-the-china-study-is-flawed.aspx
(annoyingly you have to subscribe to his mailing list to read the article)

---

Common Themes/Topics

Thought I'd list these here too, I'll start with the ones I'm aware of:

-Idea that saturated fat/animal fat doesn't cause heart disease at all (skepticism of 'lipid hypothesis'), rather it's refined grains and sugars (compare to Denise's assertion based on the China Project data that wheat is more pathogenic/carcinogenic than meat). Also that high cholesterol is good and dietary cholesterol is a nutrient.

-"healthy animal protein" to counter Campbell's assertion that all animal protein is carcinogenic, whey protein isolate is the sole example.

-cultures that eat tons of animal products are healthy/disease free/long lived (e.g. Masai, inuit, Tuoli, etc.)

-vegan diets are inherently unsustainable and/or unhealthy

-raw/free-range/grass-fed animal products do not pose the same health problems as cooked/pasteurized/grain-fed/factory farmed varieties

Support for Denise's work?

2010-07-14T09:55:31.000Z

I'd like to know if anyone came across a letter, a post, a message, whatever, from a professional in the field of medicine or epidemiology who openly supported Denise's work? And when I mean openly, I mean someone who signed it with name and title if any, not just a reply to a blog post with a pseudo stating that he/she is in the field.
I haven't come across one. Did you?
The only professional who have been kind enough to answer her have questioned her methodology, right?
Did Denise recognize the flaws in her work?

bananas stats course

2010-07-13T21:55:06.000Z

UPDATE: bananalysis is here!

coming soon to your own banana neighborhood!
here's your chance to learn the stats you slept through in school!!

there will be a course in statistical analysis offered right here with the expert guidance of veganmama (and anyone else who wants to pitch in) specifically geared to the interpretation of the china study data.

why should you do this course?

because:

1. you played hooky
2. you are keen on understanding how studies are designed
3. you are wanting to know how to interpret data - how these numbers all come together to make sense
4. you want to understand the inner workings of the china study better
5. you want to convincingly refute the nay-sayers
6. you have always wanted to have some quantitative expertise, but thought you just couldn't do it

here you will be able to do all these things, because not only will things be explained, you will be able to ask questions and have people personally assist you. there are several mathematically skilled individuals in this group who will be happy to share their expertise.

the mechanisms for the course are presently being formulated - you are welcome to join the process.
details should be available by next week - so stay tuned!

in friendship,
prad

=======
here are some temp links to contemplate while we put things together.

R software
http://www.r-project.org/
getting started with R
simpleR pdf minicourse using R: cran.r-project.org/doc/contrib/Verzani-SimpleR.pdf

mcdonald intro course
http://udel.edu/~mcdonald/statintro.html

3 online courses
http://onlinestatbook.com/
http://davidmlane.com/hyperstat/
http://oli.web.cmu.edu/openlearning/forstudents/freecourses/statistics

ultimate TOC

2010-07-13T20:12:50.000Z

this thread will serve as a map for this group so we can find things easily. it will be a good place to point people to if they are trying to find out what's happening regarding this china study criticism debunking.

this original post will be continuously updated.

in friendship,
prad

=====
action
=====

plan of action
actions members can undertake. comes with its own TOC.
the cause can really use some assistance here.

Blog Bandits
a group of productive pilgrims shedding light and learning throughout cyberspace

FAQ - The China Study, The China Project
help create an informative and comprehensive faq for future circulation

============
organizational
============

resources
a place to gather various useful materials relevant to this group

News on the China Study Critique front
the scoop on items of interest to this group
please post developments as they happen

A list of all 39 blog entries sharing Denise's questionable data analysis
blogs carrying various misinformation which we can examine and even remedy by posting

===================
critiques of the critiques
===================

Summaries of the other China Study critiques - volunteers needed
The place to see the essence of various china study critiques

A Critique of Denise's Conclusion
an examination of the matter by ste

Official Responses to Critics
Various responses from sources all gathered together into this single thread

A Cancer Epidemiologist refutes Denise Mingers China Study Claims
veganmama's initial response to the minger critique

I really question Denise's maths...
stephane's demonstration of how the minger analysis is statistically not done correctly

Paleo Robert presents some relevant critique of Denise's findings
robert mason's observation on the minger critique

Campbell's response to Denise's flawed analysis of The China Study
campbell's own commentary on the problems with the minger presentation

Dr Colin T Campbell's 2006 Rebuttal of the critics reviews
campbell's original post on vegsource on the inappropriate usage of data and methodology by critics of the china study

============
miscellaneous
============

campbell coalition site material
postings from the former campbellcoalition.org site

Repository for relevant publications
a list of published articles relating to diet

My response to Denise's acceptance of my offer to assist
a detailed set of suggestions on how to do this analysis properly

Summary of Denise's China Study Critique
a toc of denise's analysis with key points emphasized

Support for Denise's work?
Has there been any qualified or official endorsement for denise's critique?

Colin Campbell eCornell three course online education program
information and discussion about these courses

the b-d debate for dummies
a simplified guide to understanding the essences of the B-Denise debate

china study reading group
a place to discuss the china study

banana stats course
don't just eat bananas - learn to count them! discussion on the course.

bananalysis
this is the course of course!

Campbell's response to Denise's flawed analysis of The China Study

2010-07-12T22:27:25.000Z

I don’t have time to review every comment but did quickly scan the text. This analysis seems very impressive, especially given the writer’s young age with no training in nutritional science (see her web page).

She claims to have no biases–either for or against–but nonetheless liberally uses adjectives and cutesy expressions that leaves me wondering.

As far as her substantive comments are concerned, almost all are based on her citing univariate correlations in the China project that can easily mislead, especially if one of the two variables does not have a sufficient range, is too low to be useful and/or is known to be a very different level of exposure at the time of the survey than it would have been years before when disease was developing. There is a number of these univariate correlations in the China project (associations of 2 variables only) that do not fit the model (out of 8000, there would be) and most can be explained by one of these limitations.

A more appropriate method is to search for aggregate groups of data, as in the ‘affluent’ vs. ‘poverty’ disease groups, then examine whether there is any consistency within groups of biomarkers, as in considering various cholesterol fractions. This is rather like using metanalysis to obtain a better overview of possible associations. I actually had written material for our book, elaborating some of these issues but was told that I had already exceeded what is a resonable number of pages. There simply were not enough pages to go into the lengthy discussions that would have been required–and I had to drop what I had already written. This book was not meant to be an exhaustive scientific treatise. It was meant for the public, while including about as much scientific data and discussion that the average reader would tolerate.

She also makes big issues out of some matters that we had no intent to include because we knew well certain limitations with the data. For example, only 3 counties (of the 65) consumed dairy and the kind of dairy consumed (much of it very hard sun-dried cheese) was much different from dairy in the West. It makes no sense to do that kind of analysis and we did none, both because of the limited number of sample points and because we discovered after the project was completed that meat consumption for one of the counties, Tuoli, was clearly not accurate on the 3 days that the data were being collected. On those days, they were essentially eating as if it were a feast to impress the survey team but on the question of frequency of consumption over the course of a year, it was very different. Still, the reviewer makes a big issue of our not including the data for this county as if I were being devious.

In short, she has done what she claims that should not be done–focusing on narrowly defined data rather than searching for overarching messages, focusing on the trees instead of the forest.

I very carefully stated in the book that there are some correlations that are not consistent with the message and, knowing this, I suggested to the reader that he/she need not accept what is said in the book. In this very complex business it is possible to focus on the details and make widely divergent interpretations but, in so doing, miss the much more important general message. In the final analysis, I simply asked the reader to try it and see for themselves. And the results that people have achieved have been truly overwhelming.

One final note: she repeatedly uses the ‘V’ words (vegan, vegetarian) in a way that disingenuously suggests that this was my main motive. I am not aware that I used either of these words in the book, not once. I wanted to focus on the science, not on these ideologies.

I find it very puzzling that someone with virtually no training in this science can do such a lengthy and detailed analysis in their supposedly spare time. I know how agricultural lobbying organizations do it–like the Weston A Price Foundation with many chapters around the country and untold amounts of financial resources. Someone takes the lead in doing a draft of an article, then has access to a large number of commentators to check out the details, technical and literal, of the drafts as they are produced.

I have no proof, of course, whether this young girl is anything other than who she says she is, but I find it very difficult to accept her statement that this was her innocent and objective reasoning, and hers alone. If she did this alone, based on her personal experiences from age 7 (as she describes it), I am more than impressed. But she suffers one major flaw that seeps into her entire analysis by focusing on the selection of univariate correlations to make her arguments (univariate correlations in a study like this means, for example, comparing 2 variables–like dietary fat and breast cancer–within a very large database where there will undoubtedly be many factors that could incorrectly negate or enhance a possible correlation). She acknowledges this problem in several places but still turns around and displays data sets of univariate correlations. One further flaw, just like the Weston Price enthusiasts, is her assumption that it was the China project itself, almost standing alone, that determined my conclusions for the book (it was only one chapter!). She, and others like her, ignore much of the rest of the book. Can any other diet match the findings of Drs. Esslestyn, Ornish and McDougall, who were interviewed for our book (and now an increasing of other physicians have done with their patients)? No diet or any other medical strategy comes close to the benefits that can be achieved with a whole foods, plant based diet.

I also know that critics like her would like nothing better than to get me to spend all my time answering detailed questions, but I simply will not do this. As we said in our book, no one needs to accept at face value what I say. Rather, as we said in the book, "Try it" and the results will be what they are. So far, the reports of positive benefits have been nothing less than overwhelming.

I hope this helps, although it was written in haste.

Colin

I really question Denise's maths...

2010-07-12T13:37:56.000Z

I read part of Denise's on the China Study, and I quickly came to her quick note on stats:

A quick note on stats

Not a math whiz? No problem. The stuff here should be pretty straightforward, but in case you’re rusty or simply allergic to numbers, here’s a refresher on some of the statistics terminology I’ll
be using.

A positive correlation means two variables increase together and decrease together. For example, the more food Garfield scarfs down, the more Garfield weighs;
the less food Garfield eats, the less Garfield weighs.

A negative correlation means one variable increases while the other decreases, and vice versa. For example, the more Garfield exercises, the less Garfield weighs; the
less Garfield exercises, the more Garfield weighs.

Well... From this, I can see that Denise maybe be a math whiz, but she doesn't seem to know how to use stats, and what stats techniques apply for what.
After reading more, I saw her using excel to draw her data plots. I'm pretty sure she used it as well to calculate her "correlations".
So where did she go wrong?
First, my understanding is that in epidemiology you don't look at variables range when you look for associations between a factor and an effect. You draw a 2x2 table with your factor in the rows, and the effect in the columns. First row below the exposure threshold you define, second row above threshold. First column, no effects, second column with effect. Then you can fill in the numbers of incidence in the four cases defined, and calculate the risks and so on.
To what I see, Denise didn't take this approach, but went straight for the CORREL function in excel, plotting data in XY plots, and drawing lines basically between the data points. The CORREL function is a linear regression algorithm between all the points in your plot, to match a line between them, and the correlation factor is a measure of the scattering of the data points around that line.
First, this is not the stats techniques used in the field from all I could gather in litterature.
Second, let's take a quick example of how this CORREL function technique does not work for this purpose. Let's take a simple exponential function but with two different exponential rise, one almost linear (purple), the other really steep at one point (red).

You will agree that in both case, the Y increases when X increases (the more Garfield exercises, the more Garfield weights as she said). But the result of the CORREL function shows clearly a strong correlation for the purple case (0.991669, or 99%), and no correlation for the red case (0.534017, or 53%)! But this conclusion is driven if you use Denise's definition for correlations between factors and incidence.
On this simple example, you can see that the technique she used doesn't work. It doesn't work with the simplest data set your eyes can analyze easily, so imagine what it can do on more complicated data sets?
In conclusion, I think there is not much to talk about what she can write. All she writes is based on a wrong data analysis, so none of her conclusions can be valid. That will save me a lot of time reading it actually.
I bet if her work had been peer reviewed, it would have never made it out of her kitchen.
So, Denise, if you ever come across this, don't take it personally, but before jumping on your soap box and spreading wrong truth out, just open a couple books, and learn about what you want to do first.
Then, there are other aspects of her analyzes I'm not confident with, like using the mortality as a measure of disease incidence. I'm no epidemiologist, but I think for chronic disease or cancer that would not be the best indicator, since you can live quite some time with one, and you can die from other cause. But that may be what was in the original study, I don't know, I haven't seen it.
So I'll just stay on the math side, because she is so wrong in my opinion, that none of what she can write has any value.

My response to Denise's acceptance of my offer to assist

2010-07-12T08:01:49.000Z

Its purpose was to re-articulate the limitations of her analysis, but also to inform. Good science should prevail, after all.

********

Hi Denise,

As promised, I'm posting my response to your email [yes, she emailed me] on your site. You asked that I provide some tips on where to start and how to proceed. BTW, you mentioned "epidemiology secrets" and I just want to say: no "secrets"!! Epidemiology is just critical thinking, but with numbers. It's no different from many other disciplines. Maybe some time you can help me with writing (scientists are generally terrible writers, hehe).

Note: I've included some comments on what went wrong and how it can be corrected merely for demonstrative purposes - not at all malicious attacks, OK? This is how we all learn after all. In caps, I will highlight steps in the action plan for you.

STEP 0: Do a literature search. I find it helpful to keep an excel spreadsheet with columns for author, title, journal, year, summary of paper, strengths of the study, weaknesses, and concluding remarks. This is essential, as one shouldn't just blindly go into an analysis without having at least some background information on the subject matter. No need to be an expert, but good to know what's already out there, and what needs to be done.

1. Correlations:

For this discussion, the outcome will be colorectal cancer, since you used it on your post. Similarly, the primary exposure of interest will be total cholesterol. By by basing your conclusions on uncorrected correlations alone, you've made a huge leap that doesn't have much ground to stand on. The simple correlations are biased, as you yourself pointed out when evaluating total cholesterol, schistomiasis, and colorectal cancer. As such, if you don't adjust for potential confounders via multiple regression, the association you observe is biased. We almost always need to adjust for confounders, and this is very true in your case.

STEP 1: It's a good habit to evaluate the correlations between all exposures and also between all exposures and the outcome <i>at the individual level</i>. So, for *every* analysis you plan on doing, run create scatterplots for every X against X and every X against Y, using the *individual* data (where possible), and provide the correlation + 95% confidence interval for each.

STEP 2: Create histograms for every exposure of that is categoric and density plots (or you can create histograms with very narrow bars) for every exposure that is continuous. This will tell you how the variables are distributed and what the appropriate summary statistics for them would be. For example, if total cholesterol is not normally distributed (follow a bell curve) then *median* total cholesterol might be a better summary statistic then *mean* total cholesterol (good to know when you present descriptive statistics of the data you're using). Sometimes it's useful to present different stats for a single variable.

2. Individual data vs. aggregated data:

You stated you didn't see much curvature, but keep in mind that you were presenting with aggregated data (eg. average total cholesterol for all individuals) instead of including individual-level data (the exposure and outcome for a single individual). Consequently, there was a big loss in information, and you can't make accurate decisions on how to model your data if you plot aggregated data. Related to this, your analysis was ecologic (used aggregated/grouped data) but you made individual-level conclusions when you used the term "risk factor." This is referred to as an ecologic fallacy - and it's just that. A fallacy. For example, all we can say based on your cholesterol-colorectal cancer example (the one that doesn't account for schistomiasis) is that the counties with higher mean total cholesterol tend to have higher incidence rates of colorectal cancer. We can't make the leap to calling cholesterol a *risk factor* for colorectal cancer.

STEP 3: Don't aggregate your data in your analysis. Why? You lose A LOT of information when you aggregate data and you can bias your results. So keep that data at the individual-level. For descriptive tables, by all means, aggregated data is necessary for obvious reasons. But in your analysis, individual-level data when you've got it is essential.

3. The right regression model:

One of your outcomes was incidence rates of colorectal cancer. When you do your analysis with individual-level data, with incidence rates of colorectal cancer as your outcome, linear regression = WRONG model. Make sure you know which models to use and when. To start - when modeling "raw" rates (case counts and person time), we almost always use Poisson regression, and often we need to account for overdispersion as well. Get to know some of the other common regression models as well.

STEP 4: Write out all of the primary exposures of interest you want to investigate and the corresponding outcome of interest and how you're setting up your outcome variable (are you interested in colorectal cancer *incidence rates*, *prevalence*, a simple yes/no the person has colorectal cancer?)

STEP 5: Write out what the appropriate regression model would be for the different analyses you plan to conduct.

4. Confounders:

These are factors that are related to the exposure and the outcome of interest such that *not* adjusting for them will produce a biased association between exposure and outcome. As you saw, schistomiasis might be a confounder. And in fact, county might be too - and is actually upstream of schistomiasis in some sense, right? Two confounders that almost *always* must be included in a model are AGE and SEX (provided your analysis isn't restricted to one sex). This is especially true for chronic disease (eg. cardiovascular disease and cancer). In this particular case, body mass index (BMI) would be very important to include as well. County may also be important.

STEP 6: For every analysis you do, write out all potential confounders you can think of and why. You know the data better than I do as you've worked with it extensively. And, from STEP 0, you'll know your context.

STEP 7: Write out *how* the confounders are related to the exposure and outcome. Is the confounder protective (i.e. decrease risk) for the outcome? Or is it a risk factor? How is it associated with the primary exposure of interest? This is where those scatterplots in STEP 1 come in handy! The purpose of this is to give you an idea of *how* an observed association might be biased if you *don't* adjust for certain confounders. It is tedious, but thorough and, like STEP 6, will allow you to approach your analyses with more contextual background.

5. "Cleaning" and "recoding" your data:

Raw data is not *in and of itself* a bad thing. It is simply the data in its original form. But in order to be useful for analysis we often need to "clean" it and "recode" it. When I say "clean" it, I mean setting up the *dataset* that is free (to the greatest extent possible) of unnecessary data (for example, if you're interested in ovarian cancer, you wouldn't include men), or mistakes (for example, if an individual in the data was coded as being a man with ovarian cancer, this is clearly wrong). In this case, you might either omit it since you don't have a way to check which is correct or, based on other data for that individual choose to change "man" to "woman" or "ovarian cancer" to "no ovarian cancer." "Recoding" means setting up the *variables* to be useful. For example, we might recode BMI in categories of underweight, normal, overweight, and obese rather than leave it as continuous. Some variables may already be categoric, if the corresponding data were collected that way.

STEP 8: Clean your data. You will likely need to set up multiple datasets.

STEP 9: Write out *how* you've cleaned your data. (This is good record keeping.)

STEP 10: Recode your data. This might include combining variables too.

STEP 11: Create a "data dictionary" similar to the one on the Oxford site. But in addition, include a description of how you've coded your data (eg. 1=underweight, 2=normal, 3=overweight, 4=obese). Again, good for record keeping, but also "keeps you honest" so others know how you set up your data. This will often be apparent when you present your results, but not always. It's a good habit to keep track of this, in any event.

STEP 12: Replot all newly *categorized* variables against the outcome(s) of interest. Why? Because the categorized data may reveal non-linear relationships with the outcome (in fact, this is a strength of categorizing data - that we can account for some non-linear relationships). For example, underweight might be a risk for something, whereas normal BMI is protective, while overweight and obese are a risk ("U-shaped").

6. Exploration of your data through descriptive statistics:

Almost all scientific papers start out with a "Table 1" which presents a description of the data. It tells us things like What's the % of women and men in our data, What is the proportion of people with and without the exposure and with and without the outcome?

STEP 13: Create descriptive tables of all relevant variables. This includes your primary exposure of interest, confounders, and outcome. Obviously, you will have different tables for each analysis as you're interested in different primary exposures (cholesterol? meat? total caloric intake?) and outcomes (cardiovascular disease? colorectal cancer? bladder cancer?). To save time, you might include all relevant exposures and confounders in rows, and cross-classify them with all outcomes of interest in columns.

6. Analysis:

The fun part.

STEP 14: Run your models. Keep track of what you include in your models b/c oftentimes we will evaluate several models for each analysis depending on what's called "fit statistics." Since you are familiar with p-values and I assume interpretation of beta coefficients, use these to help inform you of which variables to include in your final model *within the context of the analysis at hand* (this is key - if you have reason to believe that a confounder is important to include, keep it in the model even if it's non-significant).

STEP 15: Create tables for results from *all* analyses (including the models you decide to can in favor for another one) and what regression model was used. This is much more transparent than simply producing your final model.

There's more "post-analysis" stuff that should be done, but really Steps 1-15 is a pretty thorough.

7. Publish:

I can't stress this enough. This is a long-term goal for sure, especially as you will likely end up with multiple papers! But once you think you've got the data set-up and analyses down, you need to write it up and send it on for peer-review. Peer-review is not perfect for sure, but it is the best measure we have for good science. It gives credibility to your efforts. Besides, you *do* want to be acknowledged for your efforts, right? By publishing in a peer-reviewed journal, you're more likely to gain more widely publicized attention, which I think should be the goal of most epidemiological studies; we want to improve public health through informing not only our peers, but also the public.

As a last note, I know this is a huge undertaking, but these are steps to a thorough analysis. I have no doubt you're capable of tackling it.

Best wishes.

PS. I'm sure you already planned to do this, but make all of the above available. With your large readership you can make this a collaborative effort.

A list of all 39 blog entries sharing Denise's questionable data analysis

2010-07-12T06:01:49.000Z

http://freetheanimal.com/2010/07/the-china-study-smackdown-roundup.html

The China Study exposed: actual data does not support vegetarian health claims (at Hunter-Gatherer)
China Study Problems of Interpretation (at Whole Health Source)
Polish a turd and find a diamond? (at PaNu)
The China Study: Junk Science and Lies (at Robb Wolf)
There is no justification for a plant-only diet (at Conditioning Research)
Rest in peace, China Study (at The Healthy Skeptic)
“The China Study”, Debunked (at Theory to Practice)
"The China Study: Fact or Fallacy?" (at Let Them Eat Meat)
Destroying China (the Study that Is) (at Aspire Natural Health)
The China Study Discredited (at Food Renegade)
The Study Everyone Talks About: Part 2: The Ravaging Reviews (at Feasting on Fitness)
Debunking The China Study (at Crossfit 1776)
The Debunking of the China Study (at TJ's Gym)
Thoughts on Friday from the middle of the road! (at A Moderate Life)
A Critique Worth Reading (at For His Glory & for Our Good)
"T. Colin Campbell’s The China Study: Finally, Exhaustively Discredited" (at Crossfit Peachtree)
The China Study: Crushed by its Own Data (at The Spark of Reason)
China Study & T. Colin Campbell: Someone just made you their vegan bitch (at Paleo-ish)
The China Study: Evidence for the Perfect Health Diet (at Perfect Health Diet)
The China Study Has No Clothes: Smackdown Of T. Colin Campbell (at Nutrition and Physical Regeneration)
The slam-dunking of "The China Study" (at the shmaltz)
China Study Shakedown (at Natural Messiah)
The China Study Toppled – A Tale of the Confirmation Bias (at Lean, Mean, Virile Machine)
Slaying of a Hypothesis (at Animal Pharm)
"Epidemiology is Bogus" (at Evolutionary Psychiatry)
China Study Unveiled -- Not Supporting Veganism (at Primal Wisdom)
China fiction? (at The Heart Scan Blog)
The China Study - A Superb Analysis (at Primal Muse)
Chipping Away at the China Study (at Liberation Wellness)
The China study: Debunked (at Food, flora and felines)
Buh-bye, China Study (at The Low-Carb Curmudgeon)
China Study Debunked (at The Red Pill)
Around the Fitness Horn (at x lyssa)
RAW FOOD SOS sobre o China Study (at Canibais e Reis)
Die veblüffende Biegsamkeit von Fakten: The China Study (at Urgeschmack)
Veganbibelns fall (at Kostdoktorn.Se)
Weekend Link Love (at Mark's Daily Apple)
The China Study (at Kat's Food Blog)
Debunking junk science: goodbye china study (at abundant brain & health)

Paleo Robert presents some relevant critique of Denise's findings..

2010-07-12T05:37:36.000Z

You can read the rest of this discussion by following this link...

http://disqus.com/PaleoRobert/ (Good news Robert is actually a vegan!)

Im going to try to recruit him as I feel he would be great to have on the team.

Here is his blog...

http://paleovegan.blogspot.com/

I visited this site to share our recent findings on Denise's study.

This is from the blog "Letthemeatmeat", below is a portion of the discussion between the Author of the blog post and "Paleo Robert" Sorry it may be a bit disjointed but I found it challenging to copy and past the comments in the proper order.

Robert:

These two links give some solid advice.
-- http://www.dissertationadvisors.com/articles/ge...
-- http://answers.yahoo.com/question/index?qid=200...

To help her chances even more, I'd suggest she first send her manuscript to a few experts in the field for review, including at least one who is likely to disagree with her initial hypothesis (maybe even Campbell himself). It might seem odd to ask one's adversaries for help, but scientists do it all the time; they're trained to separate their own ideas from evaluations of the merits of others' work. An honest critic is far more likely to help you spot weaknesses in your methods than is a like-minded researcher, however competent.

I wish her luck. Despite my respect for credentials, I champion the cause of lay researchers everywhere.

Author:

That isn't science, that's clinging to excuses to write her off. Shouldn't the lack of peer review be an incitement to questioning her claims, not just completely ignoring them?

Robert:
Nope. The burden of proof lies with those challenging the established model; those defending it are under no obligation to accept the challenger's ideas as valid at face value. Step One after data-collection and experiment is submitting to peer-review, by a recognized body of experts in the field. If the work is good enough, it gets published. If it overturns long-held ideas with better evidence, it wins awards. If it fails to cut the mustard, it's ignored or sent back for revision.

That's the standard Campbell had to meet with all his research over his career. It's the standard that all medical and scientific researchers have to meet. Anyone else who wants their work seriously considered should meet the same minimum standard.

Author:
To test her conclusions, all anyone needs is access to the original China Study data and an ability to interpret it.

Robert:
Wrong. You'd also need years of training as an expert in the relevant fields of science and medicine. It really isn't something that can be done by just anyone.

Author:
You're using the lack of peer review as an automatic disqualifier. Why not use it to say "this hasn't been fully settled -- let's see if other people can duplicate her conclusions"?

Robert:
That's what happens after peer-review, not before it.

Author:
It makes no sense to insist that she go through institutional peer-review channels for this. She's not a part of the institution that you say should be judging her claims.

Robert:
First, yes it does make sense; it's the bare minimum that all other researchers in the field -- expert or layperson -- must meet. Why should her work get special treatment?

Second, she doesn't need to be part of the institution. Laymen often get their work published, if it's rigorous enough.

Author:
Isn't putting something on the internet for anyone to question one way of submitting to peer-review?

Robert:
No. Science isn't a popularity contest, and most laypeople really do not possess the expertise necessary to do peer-review properly. There's a damn good reason people spend thousands of dollars and man-hours getting science educations. It's complicated, rigorous and difficult. The general public really can't do it as well as trained experts can.

You wouldn't let just anyone diagnose your medical problems, or work on your car, would you?

Author:
Have you read her China Study entries?

Robert:
Not in detail. Her work looks fairly rigorous on its face, and she is clearly a very intelligent person. However, I have not checked her math, and I don't possess the qualifications necessary to evaluate her work the way an expert in the field could (neither, I suspect, do most readers of her blog or this one). While it's possible that she has made an important discovery, one should not assume so just because one likes what they're reading. Skepticism should be the default mindset towards all authoritative claims... especially if you're inclined agree with them (because it's easier to fool yourself otherwise).

Author:

Simply hide behind a non-existent "scientific" consensus, and the fact that she's not working within a flawed, and sometimes, biased system.

Paleo Robert:
Lack of peer-review is a perfectly legitimate basis on which to decline considering her points further. Bloggers never seem to get that this is the first standard by which all scientific claims are and should be measure. Sure, it's imperfect (what institution isn't?), but it's the best we've ever done. And it is astonishingly effective at both self-correction and BS-detecting over the long term.

Author:
The book which she is critiquing is not itself a peer-reviewed publication.

Robert:
Well, by your standard, why should it need to be? And anyway, all of its original research was peer-reviewed (this was Oxford and Cornell, remember).

Author:
The China Study is constantly held up as a banner around which the vegetarian/vegan community rally, and is a very large part of what you call the current scientific consensus. To see it exposed for what it truly is- an agenda driven piece of work that draws dubious conclusions from a study that could have done so much to help people the world over- is heartwarming.

Robert:
Except that it hasn't been exposed as any such thing. Again, until its critics can produce critiques they're willing to submit to peer-review, I decline to take them seriously. If they can't mean even this minimal standard, there is no good reason for anyone to do so.

And didn't you just tell me that the consensus was non-existent?

Table of Contents / Summary of Denise's China Study Critique

2010-07-11T23:43:45.000Z

For your convenience (6 parts):

Part 1: “Meat and Disease”:

http://rawfoodsos.com/2010/06/01/a-closer-look-at-the-china-study-meat-and-disease/ (includes introduction + main points of criticism against Campbell)

Part 2: “Fish and disease”

http://rawfoodsos.com/2010/06/09/a-closer-look-at-the-china-study-fish-and-disease/

Part 3: “Eggs and Disease”

http://rawfoodsos.com/2010/06/18/a-closer-look-at-the-china-study-eggs-and-diseas/

Part 4: “Dairy and Disease”

http://rawfoodsos.com/2010/06/20/a-closer-look-at-the-china-study-dairy-and-disease/

Part 5: “Tuoli – China’s Mysterious Milk Drinkers”
http://rawfoodsos.com/2010/06/23/tuoli-chinas-mysterious-milk-drinkers/

Part 6:: “The China Study: Fact or Fallacy”

http://rawfoodsos.com/2010/07/07/the-china-study-fact-or-fallac/

Includes point by point rebuttal of Campbell’s summary of the China Study findings (found here http://www.news.cornell.edu/chronicle/01/6.28.01/china_study_ii.html) based on her interpretation of the China Project data. Also includes Denise’s concluding remarks.

News on the China Study Critique front

2010-07-11T21:16:55.000Z

Post any updates here folks, thanks!

plan of action

2010-07-11T20:18:21.000Z

it's terrific to see this group fill up with so many in such a short time!

thanks should go to gvn2fly_kristin who first challenged the notion that the china study was a pack of lies. when this matter was brought before freelee, she immediately started this thread you are all no doubt aware of:
http://www.30bananasaday.com/forum/topics/the-china-study-dr-campbell?xg_source=activity

you see many interesting exchanges in that thread - brought to a devastating exposition by the contribution of veganmama18 (freelee, who brought her onboard, has put up her post here as well).
so then i wake-up only to find freelee has been at it again, starting this group!

now i think we should organize some sort of approach to what we do here by using this thread to contribute our plan of action.

given the skills and enthusiasm of people here, if we work together, i think we can create some powerful documents that will not only counter the nonsense against the china study that circulates through the blogs, but will reveal them for the utter absurdities they are.

a toc of the actions suggested and their status is below.
just help yourself - but post if you are undertaking an action.

in friendship,
prad

===========
list of actions
===========

rayna's article repository
a collection of articles relevant to health and animal protein consumption
status: to be done
http://www.30bananasaday.com/xn/detail/2684079:Comment:636149

mandarinpeel's faq
create an faq document questions and short explanations
status: to be done
http://www.30bananasaday.com/xn/detail/2684079:Comment:629891

prad's find the pattern
identify common patterns in various critiques
status: to be done
http://www.30bananasaday.com/xn/detail/2684079:Comment:629182

eric's letter idea
create a letter with key links on the debunking to go to blogs
status: to be done
http://www.30bananasaday.com/xn/detail/2684079:Comment:629095

freelee's generic response
circulate on blogs that denise's analysis stems from misinterpreted data
status:on going
http://www.30bananasaday.com/xn/detail/2684079:Comment:629872

dan's get the report
get the copy of data
status: done
http://www.30bananasaday.com/xn/detail/2684079:Comment:629136

B's blog identification
acquiring links of blogs circulating denise's work
status: done
http://freetheanimal.com/2010/07/the-china-study-smackdown-roundup.html

A Cancer Epidemiologist refutes Denise Mingers China Study Claims due to incorrect data analysis

2010-07-11T12:04:21.000Z

From Denise's blog...

"An exhaustive analysis of the raw data from the China Project by

educator and freelance writer Denise Minger [41] shows that Campbell
failed to take into account other disease-causing variables (increased
Hepatitis B and schistosomiasis infection and rates, industrial work
hazards, etc.) that tend to cluster in higher-cholesterol counties in
the China Study. Campbell also omitted data showing a higher
correlation between wheat flour intake and many diseases (notably
coronary heart disease, cervical cancer, hypertension and stroke) than
with animal protein intake."

Read more...

http://rawfoodsos.com/2010/07/07/the-china-study-fact-or-fallac/

The following is from an Epidemiologist that refutes Denise Mingers China Study Claims due to incorrect data analysis...

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.) See point 1 below for more on this.

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. 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. While we certainly look at correlations between all factors (i.e. between the exposure, potential confounding factors, and the outcome), 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 evaluating total cholesterol and colorectal cancer (as Denise does). Sure, it can be informative, but it doesn't tell us anything about some of the other factors that might be related to cholesterol and colorectal cancer. And while she does perform a stratified analysis (stratifying on schistomiasis), which is a form of "adjustment for confounding"), it still does not take into account other possible confounders and still only tells us about general patterns, but nothing of individual-level associations. Furthermore, she doesn't present results for regions with schistomiasis. What if there was also little correlation between cholesterol and colorectal cancer in these regions? There might be other factors unaccounted for.

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.

** Updated to include an example from Denise's analysis rather than my original example of fat consumption and breast cancer.

I just realized that there's still some trail left about "fat consumption and breast cancer". I should clarify. Denise looked at cholesterol level in each Chinese county and the corresponding incidence rates of colorectal cancer in that region (this is what makes it "ecologic" - each dot represents a county). But the statement still stands - we can't make individual-level conclusions about cholesterol, colorectal cancer, and schistomiasis.

I also just want to add that when she refers to "statistical significance", all that's being tested is the "null hypothesis" that there is no correlation (i.e. correlation = 0). it is not testing whether an exposure is or is not arisk factor for the outcome, even though Denise uses this term loosely.

Dr Colin T Campbell's 2006 Rebuttal of the critics reviews...

2010-07-11T11:42:35.000Z

Sales of our book, "The China Study…", co-authored by me and my son, Tom, have exceeded our expectations. With no formal public relations campaign, no recipes, no menus and many scientific references, we were cautioned by some in the publishing world not to expect a market of more than 5-15,000 copies sold. Instead, we sold more than 100,000 copies in about the first 18 months. We also are delighted with the 300 or so on-line reviews and e-mails that speak of very positive personal health benefits.

But, not all readers agree. A small number (5-10%) have not only disagreed, but have done so rather vigorously and vehemently. I have accepted with interest these commentaries, mostly assuming that our book must be having an impact.

I have not been inclined to respond to these relatively few critics. Yet, a few friends and colleagues have asked that I consider doing so, especially because a couple of the seemingly well-researched commentaries are being vigorously promoted far and wide. These include, for example, commentaries by Mr. Chris Masterjohn (Amazon and Barnes & Noble websites, July 4, 2005, and http://www.cholesterol-and-health.com/index.html) and an anonymous Mr. "JayY" (Amazon website, May 18, 2006, with numerous additional "comments" on many positive reviews on Amazon.com). So, here goes my rebuttal.

http://www.vegsource.com/articles2/campbell_china_response.htm