Opposite of an open mind

[Cakeface]

Now that is a more sensible way of showing the data. Having the countries laid out in order of CHD prevalence and plotting other factors against it.
Sorry If my previous post implied dishonesty on your part in any way Nu, that was not my intention. I could possibly have worded it better.

[NU_nutrition_TS]

I agree that the combined graph (sorted in ascending order of CHD deaths) seems more telling than the other two. But as I was interested to see what effect on CHD mortality increasing or decreasing intakes of dietary sugar or fat would have so it seemed appropriate to order the data accordingly in the other two separate graphs. Since the data for dietary elements were combined in one table it was obviously easier to plot those against a a trend of increasing CHD mortality.

Taken together, all three graphs do seem to corroborate each other and fly in the face of the mainstream contention that increasing fat intake (not sugar) is the main driving force in CHD! It is also interesting the British and American Diabetic Associations do not prohibit diabetics (who are at greater risk of CHD and stroke) from consuming carbs or sugar as long as they adjust their medication or insulin shots to compensate but do advise them to limit their fat intake (especially saturated fats!).

BTW no offence was taken from your previous post as I was always intending to combine the data to see if the apparent trends held up. Incidentally the CHD figures (as I said) are the same BHF/WHO statistics (latest available) and the fat and sugar statistics were from different sources - the sugar statistics actually being from dental caries statistics rather than heart disease so, hopefully, makes it a little more 'independent' and free from bias!

[NU_nutrition_TS]

Yet another example of how statistical analysis of study data can result in distorted or biased conclusions courtesy of Dr Michael Eades' blog:

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Posted: 17 Oct 2008 03:29 PM CDT

`I quite agree with you,’ said the Duchess; `and the moral of that is–Be what you would seem to be–or if you’d like it put more simply–Never imagine yourself not to be otherwise than what it might appear to others that what you were or might have been was not otherwise than what you had been would have appeared to them to be otherwise.’

`I think I should understand that better,’ Alice said very politely, `if I had it written down: but I can’t quite follow it as you say it.’

Lewis Carroll


If you tell a lie big enough and keep repeating it, people will eventually come to believe it.

Dr. Joseph Goebbels, Nazi minister of propaganda


I’m starting this post with two apropos quotes. The first, from Alice in Wonderland, because the post will be a little difficult to understand; the second because I just read for the umpteenth time the Big Lie about low-carb diets and wanted to blog about it but couldn’t until I wrote this post first.

Intention-to-treat analysis (ITT) has become the de riguer way of looking at experimental results that more often than not gives erroneous results. These erroneous results are then reported as gospel, when in reality they are simply erroneous. When unbiased, intelligent people (the readers of this blog, for example) consider ITT, they cannot understand how it can be used by scientist trying to make sense out of their data, but, unfortunately, it is in almost every experiment. Here is how it works.

Let’s say were going to do an experiment comparing two different diets. We round up 100 subjects and randomize them into two groups of 50. We put one group, Group A, on one diet, Diet A, and we put the other, Group B, on a different diet, Diet B. We keep both groups on their respective diets for 8 weeks to see what happens.

At the end of the 8 weeks we find that 30 members of Group A dropped out, but those who hung in there lost an average of 3 pounds per week for a total of 24 pounds each over the course of the study. We look at Group B and find that no one dropped out of the study and that all the subjects lost an average of 1.2 pounds per week.

What does this data tell us? It’s pretty simple. It tells us that Diet A is much more effective, but is more difficult to follow. It tells us that Diet B is less effective but easier to follow. Right? All intelligent people could agree on that. So that’s how this study would be presented if it were published in a journal, right? Uh, no.

No?

No. If published, the conclusion would be that both diets are exactly the same.

Say what?!?!?

Yep. That’s what the authors would conclude. Why? Because they would use an intention-to-treat analysis. In fact, the peer-review process would probably demand it.

An intention-to-treat analysis demands that all subjects remain in the data pool, even if some have dropped out. The intention was to treat all the subjects, so the analysis should contain all the subjects, even if some left the study after the first day. In an ITT, researchers pretend that subjects who chose to abandon the study really didn’t and include them in their final data. Sounds like something from Through the Looking Glass, doesn’t it?

Let’s look at how this would work in our dietary study above. The 20 subjects in Group A who followed Diet A lost 24 pounds each. Multiply this 24 pounds times the 20 subjects who stayed in the study and you find that the group lost 480 pounds over the course of the 8 weeks. Now divide this 480 pounds by the 50 subjects who started the study, and you get a weight loss of 9.6 pounds for the 8 weeks. Dividing by 8 gives us an average weight loss of 1.2 pounds per week for all 50 subjects in Group A. Which is exactly the same as the weight loss in the subjects in Group B. So, according to the dictates of ITT, the study would show that both diets were equally effective. But, as we’ve seen, they’re not.

If a doctor were recommending a diet to his/her patients based on the actual findings of the study, he/she could reasonably say: Diet A is very effective but tough to follow, so if you think you can do it, Diet A is definitely the fastest way to lose weight. If you want something that will help you lose a little weight and is easy to stick to, then try Diet B.

If the same doctor recommends a diet to his/her patients based on the ITT results, he/she would say: Follow whichever diet you want - they’re both the same.

Why, you may ask, could seemingly intelligent people do something so stupid as use ITT to evaluate data? There is a reason, although it has its own problems.

We all know from experience and from talking to a lot of people who have lost weight that a lot of different diets work. People lose weight on the Ornish diet and they lose weight on the infinitely preferable Protein Power diet. And many other diets as well. So, we can reasonably assume that almost any diet will help some people lose weight. But we want to compare two diets to see which one is really the best. So, let’s do another experiment.

Continued in next post...

[NU_nutrition_TS]

Concluded from last post...

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Let’s take another 100 people and randomize them into two groups of 50, Group C and Group D. Those subjects in Group C go on Diet C and all of them do well. They lose an average of 2 pounds per week and all of them stay on the diet. The subjects in Group D go on Diet D, and most don’t do very well. As we all know from experience, it’s tough to stay motivated to stay on a diet if you’re not losing weight. So, 30 of the subjects in Group D drop out because they’re not losing. We know that any diet will work for some people, and Diet D is no different. The 20 who stay in the study are those who are losing on Diet D. And those 20 Group D subjects lose an average of 2 pounds per week.

In analyzing our data, if we remove from the pool of subjects all those who dropped out of the study, we are left with all 50 people in Group C, who lost an average of 2 pounds per week and only 20 people in Group D, who lost an average of 2 pounds per week. We would then find that both diets are exactly the same. Subjects in both groups lost 2 pounds per week. Therefore both diets are equally effective.

But is that true? Clearly not. And that is the problem that ITT was designed to deal with. But, as we’ve seen above, it brings its own errors.

So, how do we deal with the issue honestly and effectively? Easy. By explaining the data in two ways. Most people - researchers included - want to boil an issue down to a single answer, when two answers are required. ITT allows one answer - often incorrect - to two different questions. ITT is like the old TV show in which the clown Bozo always asked the little kids he interviewed something like this:

So, Bobby, tell me: Do you walk to school or carry your lunch?

Were Bozo adamant on an ITT-type analysis of the question, he could get only one answer.

Going back to our Group A/Group B diet study we can look at the data in two ways:

1. Diet A is extremely effective for those who stick with it. (Called the adherence effect.)

2. Only 40 percent of those attempting Diet A achieve the desired effect. (Called the assignment effect.)

Both of these statements are true. Both contain valuable information. But they answer two different questions. The first answers the question: what happens to people who stick to the diet? The second answers the question: What happens to people who are placed on the diet?

As Dr. Gerard Dallal writes about ITT

The fraud occurs when the answer to the question of assignment is given as though it were the answer to the question of adherence!


Instead of the conclusion that both Diet A and Diet B show the same results (when, clearly, they don’t), which would be the way it would be presented in a scientific paper demanding ITT, why not present it this way?:

The adherence effect: Subjects following Diet A for 8 weeks lost an average of 3 pounds per week whereas those following Diet B lost 1.2 pounds per week.

The assignment effect: 40 percent of those attempting Diet A remained in the study whereas 100 percent of those following Diet B remained in the study.

Conclusion: Diet A is significantly more effective (3 pounds per week vs 1.2 pounds per week) for those able to remain on the diet. Diet B is less effective but significantly less difficult to follow than Diet A. (100 percent of subjects on Diet B remained on the diet throughout the study whereas 60 percent of those on Diet A dropped out).

It just ain’t that hard to present it that way. It provides much more information than the ITT, which attempts to answer two questions with one answer.

Now, let’s look at the big low-carb lie that launched me into this post. I was reading a book that I intended to review for this blog and came across the following statement:

There is evidence from a variety of sources that [low-carb diets] work for short-term weight loss. One year after starting a diet, however, there appears to be no significant difference in success rate than that seen on any other common diet plan.


Have you heard that one before? It’s a specific variant of the old: Studies show that while effective in the short term low-carb diets show no difference in weight loss after one year than do low-fat diets. It’s the Big Lie.

It’s the last refuge argument of low-fat advocates who are getting hammered with all the data showing low-carb diets to be more effective. Yeah, well, they say, Protein Power may work in the short term, but over a year studies show it’s no better than low-fat. It’s like a cross thrust in a vampire’s face.

But is it true? It is if you believe in intention-to-treat analysis. But what if you believe in a more accurate way of presenting the data?

Let’s briefly look at a few studies published that confirm the idea that there is no difference between low-carb diets and low-fat diets after one year.

The first was published in the Annals of Internal Medicine in 2004. The conclusion of the authors was that after one year subjects

had more favorable triglyceride and high-density lipoprotein cholesterol levels on the low-carbohydrate diet than on the conventional diet. However, weight loss and the other metabolic parameters were similar in the 2 diet groups.


In the body of the paper, however, one can read the following:

The final 1-year weight change (mean ± SD) was –5.1 ± 8.7 kg in the low-carbohydrate group and –3.1 ± 8.4 kg in the conventional diet group (Figure). The difference in weight loss between the 2 diet groups was not significant (–2.0 kg [CI, –4.9 kg to 1.0 kg]; P = 0.195 before and P > 0.2 after adjustment for baseline variables). The difference in weight loss between the 2 diet groups between 6 months and 1 year was not statistically significant (P = 0.063).


But that’s all ITT blather. Let’s read the next couple of sentences:

Persons on the low-carbohydrate diet who dropped out lost less weight than those who completed the study (change, –0.2 ± 7.6 kg vs. –7.3 ± 8.3 kg, respectively; mean difference, –7.1 kg [CI, –11.6 kg to –2.8 kg]; P = 0.003). In contrast, weight loss was not significantly different for those on the conventional diet, whether they dropped out or completed the study (change, –2.2 ± 9.5 kg vs. –3.7 ± 7.7, respectively; mean difference, –1.5 kg [CI, –5.7 kg to 2.7 kg]; P > 0.2).


Let’s translate. Those who dropped out of the low-carb diet but were counted as if they hadn’t lost 0.2 kg (about 0.4 pounds) whereas those who completed the study lost 7.3 kg (about 16 pounds). Do you think the dropouts skewed the numbers? I guess so. And look at the next astounding sentence. “In contrast, weight loss was not significantly different for those on the conventional diet, whether they dropped out or completed the study…” So, there was no difference in the results of those following the low-fat diet whether they dropped out or stayed in. Had the subjects who dropped from the low-fat arm not been included, the results for that diet would have been the same. Including the subjects who dropped from the low-carb arm, however, dramatically lowered the overall weight loss of the subjects as a group, making them equal to those in the low-fat arm.

It could be accurately stated that those who remained on the low-carb diet for one year lost significantly more weight than those who remained on the low-fat diet. which, of course, refutes the Big Lie that low-carb and low-fat diets provide equal weight loss at one year.

The two other studies used to perpetrate the Big Lie that low-carb diets show no difference in weight loss after one year are the ones by Foster et al and Samaha et al in the May 2003 New England Journal of Medicine.

When analyzed by ITT, both of these studies show no significant difference between low-carb and low-fat diets after a year. But when looked at from the perspective of those subjects remaining in the study, we see a big difference between the low-carb and the low-fat arms.

In the Foster et al study using a modified version of the Atkins diet, we find a statistically insignificant 1.9 kg difference in weight loss between the two groups by ITT. But when we eliminate the drop outs and look instead at the data from those subjects who remained on the diets for the entire one year, we find a statistically significant 2.8 kg (over 6 pounds) greater weight loss in those following the low-carb diet.

In the Samaha et al study using the diet from the Protein Power LifePlan, those following the low-carb diet lost a statistically insignificant 2 kg more weight than those following the low-fat diet by ITT. Eliminating the dropouts, however, gives us a statistially significant 3.6 kg (almost 8 pounds) greater weight loss on the low-carb verses the high-carb diet after one year.**

Intention-to-treat analysis gives us the Big Lie: Low-carb diets are no more effective than low-fat diets after one year. Dr. Goebbels would have been proud.

The truth, however, is a little different and can be stated thus:

Those who follow low-carb diets for a year lose significantly greater weight than those who follow low-fat diets for a year.

After reading this post you should know more about intention-to-treat analysis than 99.9 percent of the physicians and dietitians practicing in the world today. Don’t let this knowledge go to waste. Next time you hear the Big Lie, point out the truth.

** Thanks to Richard Feinman, Ph.D. for the tabulation of these data and for our many conversations on this subject.

Source: http://feeds.feedburner.com/~r/drmik.../~3/424033670/

[NU_nutrition_TS]

Last week I pointed out several flaws in how researchers gathered data for the NIH-AARP Diet and Health Study, which has generated a slew of scary headlines such as Animal Fat Linked to Pancreatic Cancer.

I also mentioned that even without those flaws, observational studies can at best only produce statistical associations. They don’t prove cause and effect … although you wouldn’t always know that from the headlines.


When people mention that obesity is associated with Type II diabetes and therefore must cause diabetes, I’ll sometimes reply that gray hair is also associated with diabetes and suggest we start giving Grecian Formula to everyone to prevent it. That usually generates a reply along the lines of, “Come on, that’s ridiculous. A lot of people develop diabetes when they’re older and happen to have gray hair.”


That’s the good news: people don’t confuse an association with a cause when it’s obviously ridiculous. The bad news is that if an association isn’t ridiculous, researchers often do believe they’re seeing cause and effect – especially if the association confirms a pre-existing bias.


Since observational studies produce so many alarmist headlines, I thought it would be a worthwhile exercise to recall just how spectacularly wrong a theory based on a statistical association can be. This is a real-world example that generated a lot of headlines back in the day.


For decades, heart-disease researchers have known what while women certainly do develop heart disease, they typically develop it later in life than men … usually after menopause. Naturally, this got the white-coat crowd wondering if female hormones – particularly estrogen – might protect against heart disease. The theory seemed to make sense: men don’t produce as much estrogen as women, and women don’t produce as much after menopause.


In the 1960s, men were given estrogen as part of a large clinical trial called the Coronary Drug Project – but that arm of the trial was stopped early because the men taking estrogen began dying from heart disease at a higher rate than men in the control group. So the theory was adjusted: estrogen appears to protect women from heart disease, but not men.


Then a major observational study gave the estrogen theory some real traction. For 15 years, the Harvard Nurses Health Study had been tracking the diets, health habits and disease rates of more than 120,000 nurses. When researchers pored over the mountains of data produced by that study, they found a startling statistic: women who took estrogen had a 40% lower rate of heart disease than women who didn’t. And women who continued taking estrogen were less likely to suffer a heart attack than women who took it for awhile and then stopped.


You can imagine the research papers and the headlines that resulted. There calls among researchers and doctors alike to start prescribing estrogen to all post-menopausal women who had risk factors for heart disease. More cautious researchers called for a controlled clinical trial before estrogen was given out like heart-healthy candy, and were criticized for it. How could they, in good conscience, deny this obvious wonder drug to millions of women while waiting for long clinical trials to play out?



A pharmaceutical company, Wyeth-Ayerst, eventually funded the clinical trials – hoping, of course, that estrogen would be shown to prevent heart disease. More than 16,000 women were randomized and enrolled in the study. For five years, half received estrogen and half received a placebo.


The results were hardly what Wyeth-Ayerst had expected: The women taking estrogen developed heart disease at a higher rate – 30% higher, in fact. They were also more likely to suffer a stroke … another cardiovascular disease. Later clinical trials confirmed the bad news.


The experts were flabbergasted. The statistical correlation in the Harvard Nurses Study couldn’t have been more convincing: women who took estrogen were far less likely to have a heart attack. And it couldn’t have been fluke – there were too many subjects involved.


So what happened? Nobody can say for sure, but some researchers at the time offered an explanation that makes perfect sense: the women in the Harvard study who took estrogen were more concerned about their health. That’s why they took a hormone replacement in the first place.



In other words, estrogen didn’t create healthy nurses, but health-conscious nurses did take estrogen. Meanwhile, the health-conscious nurses were less likely to develop heart disease … for any number of reasons.


This really isn’t all that surprising. In clinical trials, people who religiously take their pills tend to have better health outcomes than people who don’t. And guess what? It doesn’t matter if the pill they’re taking is the actual drug or the placebo. The difference is in the people, not necessarily in the pill.



Some people care about their health. Some people are lackadaisical about health. Researchers call them “adherers” and “non-adherers.” I have my own, more colorful labels. The point is, we’re talking about different kinds of people, and that difference can produce statistical correlations in observational studies that have little if anything to do with the true cause and effect.


Think about the estrogen studies again for a moment: we now know that estrogen doesn’t prevent heart disease and in fact can make it worse. And yet in a large, observational study, taking estrogen was associated with a steep reduction in heart disease – almost certainly because health-conscious women were more likely to take it.


Now think about some of the alarmist headlines and health-nanny propaganda you’ve read over the years, and ask yourself what’s really going on. Here a few examples I came up with:


Does a diet high in saturated fat cause cancer and heart disease? Nope. But since saturated fat has been demonized for 30 years, health-conscious people probably eat less of it.


Does giving up meat make you healthier? Nope. But most people who become vegetarians are probably health conscious.


Do whole grains prevent diabetes and cancer? Hell, no. But they’re less likely to cause those diseases than white-flour products, and health-conscious people are more likely to choose them.


Does watching Fat Head at least three times give you a high IQ? Uh … no. But I’d like to think there’s a strong statistical correlation.


From Tom naughton's blog: Fat Head » The Association of Misleading Studies

[NU_nutrition_TS]

Rodent of the Week: What do animal studies tell us?


Rodent of the Week is devoted to highlighting promising animal research. We shine this little spotlight on animal research because, typically, it's an area we tend to ignore. While often fascinating, animal studies are conducted at such an early stage in the research process that it's irresponsible to publicize the findings of most of these studies. Doing so can inadvertently raise hopes that the research is destined to translate into gains in human health. A study published this week in the journal PLoS Medicine speaks to this dilemma. Researchers analyzing animal research found that about one-third of animal studies led to human, randomized clinical trials. And only one in 10 of those human clinical trials resulted in therapies approved for use in humans.

The reasons animal research frequently looks good in animals but fails to pan out in humans are many. Sometimes, there are methodological flaws in the animal studies. In other words, what looked effective really wasn't. It's also possible that some things work in animals and not in humans because we are, after all, different species. Also, only animal studies that succeed may end up being published.

A good example of the problem with animal studies is in stroke research, the authors point out. In animal models, almost 500 therapies have been shown to be effective in protecting neurological functioning following a stroke. But only two treatments have been proven useful in humans.

This doesn't mean animal research is worthless. Indeed, animal research is often necessary. To minimize the number of animal-to-human research failures, however, the authors recommend that animal studies be conducted using the same high standards as those used in human trials. This means doing things like paying attention to the sample size in the study, conducting blinded experiments (when the researcher doesn't know which treatment the animal is receiving) and strict control of variables.

For the rest of us, it's helpful to remind ourselves that success in a 1-ounce, furry creature with red eyes and a tail doesn't mean we'll benefit, too.
-- Shari Roan


Rodent of the Week: What do animal studies tell us? | Booster Shots | Los Angeles Times

[NU_nutrition_TS]

Another indication of how science is compromised:

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American prosecutors are attempting to extradite a Danish scientist.

Poul Thorsen has been charged with 13 counts of wire fraud and nine counts of money laundering; a federal grand jury alleges that Thorsen stole over $1 million from autism research funding between February 2004 and June 2008.

Thorsen is said to have used the proceeds to buy a home in Atlanta, two cars and a Harley Davidson. He is said to have stolen the money while serving as the 'principal investigator' for a program that studied the relationship between autism and exposure to vaccines.

The Copenhagen Post reports:

"... [O]ver the four-year period he submitted over a dozen false invoices from the CDC for research expenses to Aarhus University, where he held a faculty position, instructing them to transfer the funds to a CDC account, which was in fact his personal account ...

Thorsen's research on autism is widely known in academic circles, where he was until this week a highly respected figure. A paper of his on the subject, which is known as 'The Danish Study', is quoted extensively to refute the autism vaccine connection."

Another prominent name in vaccine medicine, Dr. Paul Offit, well-known shill for the vaccine industry, has also been called out for making false and unsubstantiated statements about CBS News Investigative Correspondent Sharyl Attkisson and her report looking into the ties between vaccine supporters and the vaccine industry.

On April 18, 2011, the California Orange County Register issued a retraction of an August 4, 2008 article containing disparaging statements made by Dr. Offit about Attkisson.

According to Adventures in Autism:

"Upon further review, it appears that a number of Dr. Offit's statements, as quoted in the OC Register article, were unsubstantiated and/or false. Attkisson had previously reported on the vaccine industry ties of Dr. Offit and others in a CBS Evening News report 'How Independent Are Vaccine Defenders?'"

The unsubstantiated statements included a claim that Attkisson "lied", and a claim that CBS News sent a "mean spirited and vituperative" email. Offit also told the OC Register that he provided CBS News "the details of his relationship ... with pharmaceutical company Merck", but documents provided by CBS News indicate Offit did not disclose all of his financial relationships with Merck.

Now, didn't a British researcher (who first raised concerns about a link between MMR vaccines and autism in children) lose his job and reputation based on the contrary data provided by people such as this Danish researcher?

[NU_nutrition_TS]

Another report on the way studies can be compromised and make their conclusions unreliable (this time, conflict of interest):

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The Studies You Should Never, Ever Believe

Two reports, published merely days apart, present radically different answers to the question of whether or not microwave radiation is harmful. Which one should be believed, and why?

In one publication, which identified studies documenting increase in cancers with microwave exposure, the authors claim no conflict of interest.

The other publication presents a conflict of interest statement that mentions they received funding from organizations that include the Mobile Manufacturers' Forum and the GSM Association, that they hold shares in the telecoms companies Cable and Wireless Worldwide and Cable and Wireless Communications, and many other similar conflicts -- and then they, likewise claim that they have no conflicts of interest!

It may come as no surprise that the second study finds that the evidence is against mobile phone use causing brain tumors.

According to Dr. Magda Havas:

"For someone who is intimately familiar with the studies they cite I find it fascinating how they skirt around the 'inconvenient' results that don't fit their conclusions ... The studies show that after 10 years of moderate cell phone use ... there is a statistically significant increase in ipsilateral gliomas ... So who do you believe? Those with a conflict of interest who claim not to have one or those without?"

Sources:

Dr. Magda Havas July 5, 2011

Experimental Oncology 2011; 3(2): 62-70

Environmental Health Perspectives July 1, 2011

Via Dr. Mercola's blog.