the-psoriasis-treatment

As we have seen in an earlier post on the China Study data ( ), which explored relationships hinted at by Denise Minger’s previous and highly perceptive analysis ( ), one can use a multivariate analysis tool like WarpPLS ( ) to explore relationships based on data reported by others. This is true even when the dataset available is fairly small. So I entered the data reported in the most recent (published online in March 2012) study looking at the relationship between red meat consumption and mortality into WarpPLS to do some exploratory analyses. I discussed the study in my previous post; it was conducted by Pan et al. (Frank B. Hu is the senior author) and published in the prestigious Archives of Internal Medicine ( ). The data I used is from Table 1 of the article; it reports figures on several variables along 5 quintiles, based on separate analyses of two samples, called “Health Professionals” and “Nurses Health” samples. The Health Professionals sample comprised males; the Nurses H...

I am not a big fan of using arguments such as “food questionnaires are unreliable” and “observational studies are worthless” to completely dismiss a study. There are many reasons for this. One of them is that, when people misreport certain diet and lifestyle patterns, but do that consistently (i.e., everybody underreports food intake), the biasing effect on coefficients of association is minor. Measurement errors may remain for this or other reasons, but regression methods (linear and nonlinear) assume the existence of such errors, and are designed to yield robust coefficients in their presence. Besides, for me to use these types of arguments would be hypocritical, since I myself have done several analyses on the China Study data ( ), and built what I think are valid arguments based on those analyses. My approach is: Let us look at the data, any data, carefully, using appropriate analysis tools, and see what it tells us; maybe we will find evidence of measurement errors distorting the ...

The graph below shows the results of a multivariate nonlinear WarpPLS ( ) analysis including the variables listed below. Each row in the dataset refers to a county in China, from the publicly available China Study II dataset ( ). As always, I thank Dr. Campbell and his collaborators for making the data publicly available. Other analyses based on the same dataset are also available ( ).     - Wheat: wheat flour consumption in g/d.     - Aprot: animal protein consumption in g/d.     - PProt: plant protein consumption in g/d.     - %FatCal: percentage of calories coming from fat.     - Mor35_69: number of deaths per 1,000 people in the 35-69 age range.     - Mor70_79: number of deaths per 1,000 people in the 70-79 age range. Below are the total effects of wheat flour consumption, along with the number of paths used to calculate them, and the respective P values (i.e., probabilities that the effects are due to chance). Total effect...

In order to achieve muscle gain, one has to repeatedly hit the “supercompensation” window, which is a fleeting period of time occurring at some point in the muscle recovery phase after an intense anaerobic exercise session. The figure below, from Vladimir Zatsiorsky’s and William Kraemer’s outstanding book Science and Practice of Strength Training ( ) provides an illustration of the supercompensation idea. Supercompensation is covered in more detail in a previous post ( ). Trying to hit the supercompensation window is a common denominator among HealthCorrelator for Excel (HCE) users who employ the software ( ) to maximize muscle gain. (That is, among those who know and subscribe to the theory of supercompensation.) This post outlines what I believe is a good way of doing that while avoiding some pitfalls. The data used in the example that follows has been created by me, and is based on a real case. I disguised the data, simplified it, added error etc. to make the underlying method rel...

The graph below shows the mortality in the 35-69 and 70-79 age ranges for men and women for the China Study II dataset. I discussed other results in my two previous posts ( ) ( ), all taking us to this post. The full data for the China Study II study is publicly available ( ). The mortality numbers are actually averages of male and female deaths by 1,000 people in each of several counties, in each of the two age ranges. Men do tend to die earlier than women, but the difference above is too large. Generally speaking, when you look at a set time period that is long enough for a good number of deaths (not to be confused with “a number of good deaths”) to be observed, you tend to see around 5-10 percent more deaths among men than among women. This is when other variables are controlled for, or when men and women do not adopt dramatically different diets and lifestyles. One of many examples is a study in Finland ( ); you have to go beyond the abstract on this one. As you can see from the gr...

WarpPLS  and HealthCorrelator for Excel were used to do the analyses below. For other China Study analyses, many using WarpPLS as well as HealthCorrelator for Excel, click here . For the dataset used, visit the HealthCorrelator for Excel site  and check under the sample datasets area. As always, I thank Dr. T. Colin Campbell and his collaborators for making the data publicly available for independent analyses . In my previous post  I mentioned some odd results that led me to additional analyses. Below is a screen snapshot summarizing one such analysis, of the ordered associations between mortality in the 35-69 and 70-79 age ranges and all of the other variables in the dataset. As I said before, this is a subset of the China Study II dataset, which does not include all of the variables for which data was collected. The associations shown below were generated by HealthCorrelator for Excel. The top associations are positive and with mortality in the other range (the “M...

WarpPLS  and HealthCorrelator for Excel  were used in the analyses below. For other China Study analyses, many using WarpPLS and HealthCorrelator for Excel, click here . For the dataset used, visit the HealthCorrelator for Excel site  and check under the sample datasets area. I thank Dr. T. Colin Campbell and his collaborators at the University of Oxford for making the data publicly available for independent analyses . The graph below shows the results of a multivariate linear WarpPLS analysis including the following variables: Wheat (wheat flour consumption in g/d), Aprot (animal protein consumption in g/d), Mor35_69 (number of deaths per 1,000 people in the 35-69 age range), and Mor70_79 (number of deaths per 1,000 people in the 70-79 age range). Just a technical comment here, regarding the possibility of ecological fallacy . I am not going to get into this in any depth now, but let me say that the patterns in the data suggest that, with the possible exception...

In previous posts on this blog covering the China Study II data we’ve looked at the competing effects of various foods, including wheat and animal foods. Unfortunately we have had to stick to the broad group categories available from the specific data subset used; e.g., animal foods, instead of categories of animal foods such as dairy, seafood, and beef. This is still a problem, until I can find the time to get more of the China Study II data in a format that can be reliably used for multivariate analyses. What we haven’t done yet, however, is to look at moderating effects. And that is something we can do now.  A moderating effect is the effect of a variable on the effect of another variable on a third. Sounds complicated, but WarpPLS  makes it very easy to test moderating effects. All you have to do is to make a variable (e.g., animal food intake) point at a direct link (e.g., between wheat flour intake and mortality). The moderating effect is shown on the graph as ...

The “great blogosphere debate” rages on regarding the effects of carbohydrates and insulin on health. A lot of action has been happening recently on Peter’s blog , with knowledgeable folks chiming in, such as Peter himself, Dr. Harris, Dr. B.G. (my sista from anotha mista), John, Nigel, CarbSane, Gunther G., Ed, and many others. I like to see open debate among people who hold different views consistently, are willing to back them up with at least some evidence, and keep on challenging each other’s views. It is very unlikely that any one person holds the whole truth regarding health matters. Unfortunately this type of debate also confuses a lot of people, particularly those blog lurkers who want to get all of their health information from one single source. Part of that “great blogosphere debate” debate hinges on the effect of low or high carbohydrate dieting on total calorie consumption. Well, let us see what the China Study II data can tell us about that, and about a few other things....