Probability fudging-drug companies with too many vaccinations, Randomized Controlled Trials (RCTs), airplanes safer than cars

In this article I cover some examples of probability fudging which goes on around us daily. The basic tenet of this post is:
When probabilities of two things are low (less than 1%), the comparison between these two things may makes you feel that your intended therapy or treatment improves things; but that doesn't mean it should be done, because 
1) there is a cost of doing interventions, of hassling a large fraction of the population to save a few. A crude example: We can be safer from a meteor hit by walking around in a body armor all day; but the probability of a meteor hit being low, it is not something necessary to do. There is a cost of making people mandatorily walk around in body armor all day, it is darn uncomfortable, and must be considered in the equation. And,
2) there is a safety profile of interventions, you may be doing more secondary harm by the intervention than helping the few who are helped.

HPV and rare disease vaccinations

There is a lot of talk about Human Papillomavirus (HPV) vaccinations. Kids, especially girls, between the age of 10 to 15 years are recommended to get this vaccine. Let us look at the data, published by CDC here.

There are 79 Millions Americans (total population of about 300 Million) affected with HPV. There are 12 Million cases of new infections per year. Cancers attributed to HPV are 27000, of which 18000 are girls, the rest of 9000 being boys.

Since 1 in 4 Americans has the virus already, and 12 million get it every year, the virus itself can't be that bad. This is a classic case of measuring too much-if common bacteria presented in the mouth are measured, surely 1 in 4 have some particular "infection"...most Americans get on with their lives just fine, and HPV infection, even if they don't know about it, doesn't seem to be a big deal in everyday life. From this data, it is more common than the common cold virus-and you begin to wonder how (and why) they collected this data at the first place. But let's trust the data for a moment anyway.

The cases where you have severe effects (cancers) are interesting to us, 27000. It is a large number, but looking at the overall population of HPV infected people (79 Million), it is a very small percentage, about 0.0034%.   Only 3 in 10000 people are getting cancers attributable to HPV.

The pharma companies say we can eliminate this tiny percentage of people getting cancers by injecting them with the HPV vaccine. The side effects of thee vaccine appear on the same page: Out of 67 million doses of vaccine, 25000 people reported some side effects, and 2000 of these were serious. The serious side effects are about 0.00025%, or about 0.25 in 10000. We must remember that many people may not report a problem even if their child has some side effects, so this number 25000 is likely to go up.

At first instance, it will look like the benefit of the vaccine, eliminating 3 in 10000 cancers, is about 10 times better than the serious side effects of the vaccine (0.25 in 10000). But if the disease is rare to start with, like this HPV-cancer, does it make sense to vaccinate at all? There is tremendous hassle and cost involved in vaccinating children, and society has to bear that cost, one way or the other. Obviously what's rare and not rare is subjective, but to me, a disease which will going to happen to 3 in 10000 is quite rare, and we should not hurry to vaccinate kids against it. Couple that with the problem with this specific case where there's no clear indication that the HPV is causing the cancer (they are confusing correlation with causality, and assuming that the HPV vaccine with prevent cancers 30 years from now, which is very speculative an assumption) and you see that HPV should not be a mandatory vaccine.

Look at the distribution of 1-x, not of x

When probabilities are very low, the right distribution to look at is the 1-x distribution. That is the statistical trickery these guys are doing to convince us of the wonderful effects of the vaccine. If x is small, less than 0.1% (1 in 1000), you must evaluate 1) the risk of intervention causing more damage then the disease itself (the safety of the treatment or vaccine), and 2) just the hassle and cost of everyone undergo the intervention. Unless you have clear data that there is a strong benefit, don't administer vaccines (or other procedures).

Let us look at the 1-x distribution of the same data (placebo group).

99.9966% will not develop a cancer related to HPV if they are not vaccinated.
100% will not develop a cancer related to HPV if they are not vaccinated, but 0.00025% will get serious side effects.

By focusing on the "x" distribution, the benefit is magnified; but in reality, the real distribution of interest is the 1-x, which is a stable distribution, and doesn't change much by vaccinating our children. And we subject the large number of 1-x to an unnecessary intervention/vaccine.

Randomized Controlled Trials (RCTs) are not what they claim them to be 

Randomized Controlled Trials (RCTs) are hardly a gold standard they are touted to be.

If you do RCT of insulin injections on the whole population, RCT will show it is a great idea, because some people (the diabetics) really will be helped by insulin. But such an RCT will now suggest EVERYONE be given insulin injections.

If you don't target your specific subgroup (diabetics), you basically overdo your therapy. It is statistically valid, but subjects a lot of people to unnecessary treatments.

The central problem is that small differences can be statistically significant, and an RCT will pick them up. It will then recommend a therapy for EVERYONE. While insulin injections will do no harm to the non-diabetics, they are completely unnecessary for them.

Most vaccinations are for diseases which are rare. Instead of targeting the small number of people who are vulnerable, vaccines are given to the whole population. This is if you believe virus/vaccination theory to be true (which I don't anymore, by the way). It is just statistical trickery passing off as science/medicine. In common parlance, you are burning the house if you find a rat in it.

Whenever only a small percentage (less than 10%) of a group has a disease, it is unfair to subject 90% to a treatment or vaccination. One must try to figure out with is common in this subgroup, and do the treatment or vaccine trial on this subgroup. When it is less than 1%, it really is bad medicine to subject the 99% to something which they will not even be effected by.

Airplane and Car driving safety

Airplanes are safe, driving in cars is safe. Most people know this, and will take a plane or car ride from a place to another without thinking about safety-they will only worry about costs and the conveniences and inconveniences when comparing the two. However, you have all sorts of bad statisticians comparing airplane safety to car safety, and concluding that airplanes are safer (or unsafer) than driving. The error there is that the 1-x is the real distribution of interest: the probability of survival. That is maybe 99.95% over 10 years of car driving  to 99.99% for plane riding, and those two are similar, dont you think? One thing is 99.99% safe, the other is 99.95% safe...we can agree that they are both quite safe. No one thought that planes are significantly safer or unsafer than cars. Both are safe, the 1-x is the real distribution.

Related articles: 

No comments:

Post a Comment