« How to Consume Big Data (from McGraw-Hill blog) | Main | Causal thinking »


Feed You can follow this conversation by subscribing to the comment feed for this post.


While a hamburger is processed, it is still red meat. The processed meats all have something done to them involving cooking and usually adding chemicals. That is why they are treated differently.

Harcombe doesn't seem to like the idea of multivariable adjustment, but it really does work. One of the problems in this data is that age varies between the meat consumption quintile groups. Once you correct for age there is a trend for risk with total meat consumption. Correcting for the others has some problems but generally seems safer. I'm not going to think about what all the path implications of all the covariates are.

Basically with these types of analyses, it really is a question of what other covariates are associated with the factor of interest and are we successfully correcting for them and are there some that we missed. Harcombe seems to be against doing these corrections. It is also possible to overcorrect by including covariates on the causal pathway. That is if consuming meat causes the effect then we shouldn't correct for it. This probably applies partly to BMI and total energy, so it may be overcorrecting. There are probably other things that are missed so that the model is under corrected.

One interesting result is that low meat eaters have higher cholesterol. A suspicion is that people with high cholesterol are modifying their diet, and it is having little effect on their cholesterol. It is an interesting possibility as it would reduce the apparent effect of eating meat. Harcombe seems to think that cholesterol is good. Try telling that to 40 year olds who have very high cholesterol and a heart attack.


Ken: The reason why she complained about processed and unprocessed is that the research separately estimated the impact of the two types of red meat. I skipped that detail as it's not really pertinent to my point here.

I don't endorse Harcombe's point of view on multivariate adjustments and she's painting with a broad brush. What I do like is the way she interacts with the data analysis. As a consumer, we cannot afford the time nor have the expertise to replicate everything that researchers have done but we also must not give blind trust.

As you pointed out, multivariate adjustment is based on a model and it is only as good as the model assumptions. It's a challenge when the amount of confounding is so much.


Multivariate adjustments are, indeed, very useful but can be tricky. Note Ken's comments about undercorrection / overcorrection.

Looking at the less adjusted data [Death rate (Z)] I see Q1-Q4 with nothing going on, and Q5 showing a big effect. (a threshold model) It's easy to be suspicious, at least, of an adjustment that converts this to a monotonic, nearly linear effect.


You have to use common sense as well -when you look at past large healthy populations like Japan they mostly ate rice and vegetables. Then when newer generations move to the West or as fast food chains creep in over there they start to get heart disease, cancer and other western diseases. So whether it's a bag of chips, bacon or a big mac you're best to avoid any of them I would imagine.

Low meat eaters don't change their cholesterol because they probably still eat dairy or other types of meat like fish which also have cholesterol. Saturated fats from coconut oil can also make the liver produce more cholesterol. Dean Ornish and others have shown you can reverse heart disease with a plant-based diet.

Criticizing the person for making money or being a vegan is called an ad hominem attack and doesn't usually work in rational arguments.

Verify your Comment

Previewing your Comment

This is only a preview. Your comment has not yet been posted.

Your comment could not be posted. Error type:
Your comment has been posted. Post another comment

The letters and numbers you entered did not match the image. Please try again.

As a final step before posting your comment, enter the letters and numbers you see in the image below. This prevents automated programs from posting comments.

Having trouble reading this image? View an alternate.


Post a comment

Your Information

(Name is required. Email address will not be displayed with the comment.)

Marketing and advertising analytics expert. Author and Speaker. Currently at Vimeo and NYU. See my full bio.

Spring 2015 Courses (New York)

Jan 26: Business Analytics & Data Visualization (14 weeks) Info

Feb 23: Statistics for Management (10 weeks) Info

Mar 28: Careers in Business Analytics & Data Science (one-day seminar) Register

Apr 7: The Art of Data Visualization Workshop (6 weeks) Register

Next Events

Sep: 28 Data Visualization New York Meetup, New York, NY

Oct: 5 Andrew Gelman’s Statistical Communications class, Columbia University

Oct: 13 AQR ProSeminar, NYU Sociology

Oct: 22 Leading Business Change Through Analytics, Columbia Business School

Oct: 30 Ray Vella’s Designing Infographics class, NYU

Past Events

See here

Junk Charts Blog

Link to junkcharts

Graphics design by Amanda Lee


  • only in Big Data