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Dawkins and Chopra

Unfortunately, I missed the live stream of a debate between Richard Dawkins and Deepak Chopra this weekend. I made the faulty assumption that I would be able to view / listen to it later, but for some reason that I don’t understand, youtube has blocked replay of this event in the United States. I’ve been assured that there was nothing particularly groundbreaking in this event…

Chopra says, “Blah blah, quantum, blah blah blah, consciousness, other way of knowing…”

Dawkins replies, ” What in God’s name are you talking about?”

A clip of a prior interaction between the two can be found at:

As my previous post hints, there is a problem in the communication of science to the public. It may be added that there is a deep valley between the way that scientists speak and the way that the public – or more importantly, the way some public personalities like Chopra- speaks.

Dawkins can be heard in the video clip above trying to make sense of Chopra’s language. Is he speaking in metaphor or does he mean to speak literally?

 
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Posted by on November 11, 2013 in Uncategorized

 

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An interesting question

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Paul Offit’s Vaccines course recently started up on Coursera.org and I intend to follow it through this semester an possibly use elements of this course and the discussions I find there in my own class (esp. Microbiology, but perhaps also in General Biology).

In the discussion forums someone, using the pseudonym, Amy Pond, posed a great question. “How do you decide what constitutes a reliable source of information?”

It is deceptively difficult to answer. If the question regards science, should everyone be expected to track down primary publications and review the data for themselves? If so, how do you even decide which sources to get your data from? If you admit that you do not have the time, ability or inclination to go to the data, is there anyone you can trust to give you the straight dope?

We live in an interconnected world with a surfeit of information. How can we avoid confirmation bias in our online ‘research’? Does the popularity of an opinion (The bandwagon effect) make it more or less believable? How do the search terms you use bias  the answers you receive when ‘asking google’, i.e. what about the framing of an argument?

So, how do you decide what sources to listen to? 

 
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Posted by on September 4, 2013 in Uncategorized

 

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All in a kerfuffle

I’m all bent out of sorts since I decided to write about the green coffee extract paper popularized by Dr. Oz. 

Here’s the problem: in my last post I attempted to unpack the data presented in the article describing a weight loss trial using this supplement. Yet, the closer I examined the data, the more clear it was to me that the data presented in that paper does not support any conclusions.

This does not mean that the supplement is effective or not. It doesn’t even mean that the group is lacking in data that would answer the question. It merely means that the numbers they present and the descriptions of their methods do not allow one to scrutinize the data in a way that supports or refutes their claims.

ImageFor anyone interested in a fun discussion of statistics and what they mean, I strongly recommend the classic text, How to Lie with Statistics, by Darrell Huff.It’s a bit out of date, but still a lot of fun to read and educational for those who have not spent much time analyzing figures.

One thing the Mr. Huff’s book does well is brings the reader into the discussion of data and how to present it. A lot of his focus is on how advertisers manipulate their graphs and language in order to obfuscate the truth.

I don’t think this coffee extract paper is intentionally obfuscating the truth, rather, I think the confusion comes from an inability of the authors to present their data clearly (even to themselves perhaps). I’ve worked in a number of labs with a number of scientists in my life and I can say with conviction that not all scientists ability to analyze their data is the equal. In fact, I have seen a number of presentations where the presenter clearly did not understand the results of their own experiments. I can say that sometimes I have not understood my own data until presenting it before others allowed us to analyze it together (i.e. I am not exempt from this error).

I would love to have the opportunity to examine the raw data from these experiments to determine if they really do address the question – and whether, once addressed, the question is answered. I’m going to appeal to both the journal and the authors for more clarification on this and will report my findings here. 

 

 
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Posted by on July 23, 2013 in Uncategorized

 

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Because it was on Dr. Oz, I’m more likely to think it’s a scam

doctor-ozI got something interesting in my inbox the other day. Something that I assume was a  friend’s email address getting hacked – although it’s the least offensive (apparent) hack I’ve ever seen (he says as the viruses circulate around his computer’s RAM).

It was a nearly blank email with a link to a Dr. Oz clip about the weight-loss promoting effects of green coffee extract, which contains high concentrations of chlorogenic acids. These molecules are said to promote weight loss through increasing metabolism.

Being a scientist means being a skeptic. In this case, because I already feel like it must be BS due to its connection with Dr. Oz (an Oprah-elevated proponent of many untested, ‘alternative’ therapies), the challenge for me is to admit the possibility that this stuff may work. So, rather than looking through the data to see if there’s anything to deny the claim, I’m really trying hard to look at the data to see any glimmer  of possibility.

Here’s a link to the Dr. Oz article online. The article was published in the January 2012  Diabetes, Metabolic Syndrome and Obesity, and happily the entire article is available free of charge. So let’s look at the data…

The article examines a “22-week crossover study was conducted to examine the efficacy and safety of a commercial green coffee extract product GCA™ at reducing weight and body mass in 16 overweight adults.” Half of the participants were male and half female – a typical study setup (although I do worry about how data is handled when looking at both sexes together, so let’s pay attention to that.)

Dr. Oz’s website indicates that “The subjects (taking the supplement) lost an average of almost 18 pounds – this was 10% of their overall body weight and 4.4% of their overall body fat.” These are pretty hefty claims, but I could use losing 18lbs, so let’s see where this goes.

The study followed those eight men and eight women for 22 weeks. At the beginning of the study, the average body mass index (BMI) at the start of the study was 28.22 ±  0.91 kg/m2 . Determine your own BMI here.

Note that BMI < 18.5 is underweight

18.5  –  25     healthy weight

25   –   30      overweight

30+               obese

This puts the study participants at the high end of overweight, but ‘preobese’.

Dosages of the green coffee extract and placebo were as follows:

“This study utilized two dosage levels of GCA, as well as a placebo. The high-dose condition was 350 mg of GCA taken orally three times daily. The low-dose condition was 350 mg of GCA taken orally twice daily. The placebo condition consisted of a 350 mg inert capsule of an inactive substance taken orally three times daily.”

I don’t think I’m the first one to point out that it’s hard to have a double blind trial when the dosages are distinguishable (two times vs three times daily). At least the placebo should be indistinguishable from the high dose.

One early eye-catching piece of data is from Table I, that summarizes the data of all precipitants as

BMI (kg/m2) pre study:28.22 ± 0.91        post study:25.25 ± 1.19     change-2.92 ± 0.85**, -10.3%

On average, all subjects lost weight during the study. But this really tells us nothing because we could see a 10% drop in BMI if the test arm lost 20% and then placebo arm stayed the same, or we could see the same thing if the weight loss occurred during ALL arms of the study.

Perhaps this reporting of data is justified by the next statement that participants all rotated through being on high dose, lose dose or placebo with intervening washout periods. Presumably, this makes the most of a small sampling of people, but I do find it harder to be confident about the data. Then again, I have never been involved in any human trial of this kind.

here’s the data:

High Dose arm:

start    BMI (kg/m2) 26.78 ± 1.55  –>    end 26.03 ± 1.36

Low Dose arm:

start    BMI (kg/m2) 26.25 ± 1.37  –>    end 25.66 ± 1.20

placebo arm:

start    BMI (kg/m2) 25.66 ± 1.20  –>  and 26.67 ± 1.72

At first glance this might appear to be pretty good. But let’s graph it out:

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the data continue to look great.

Now, with error bars:

ImageHuh. Not so hot anymore.

Also, I’m not how sure this was done, but they get p values for HD p = 0.002, LD p = 0.003, placebo p = 0.384. These stats mean that the HD and LD groups are showing very significant differences, while the placebo group is not. You should be able to see this in the graph with error bars (as an approximation of significance). Unfortunately, I see a whole lot of no nothing. But, perhaps BMI is not the appropriate way to observe weight change (we are, after all not seeing specific weight changes, but changes within a group, i.e. diversity)

Another way to try to see what’s going on is to take a look at the weight data:

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The data were presented in a number of other ways, but each of these was confusing and didn’t illustrate any clear conclusion (my interpretation).If the individuals’ data were visualized as a scatter plot, this might show us something – or data for each individuals change while in each group… As it is, we see unclear data with spectacular statistics, but we don’t get to see enough to be convinced of the changes.

Rather than go on and get more and more skeptical, let’s say, although we don’t see a lot here, the data,as reported, would make us want to see a larger study with some revisions for control of diet, exercise monitoring and a change in the way osage is administered so as to maintain the ‘blindness’ of the study.

 
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Posted by on July 22, 2013 in Uncategorized

 

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