Freedom 2 Question

Glenn Greenwald nails it again

2011-10-22T10:42:00.000-04:00

I am concerned about our nation's soul and character. Please read Greenwald brilliant observations about how there is something so very wrong about dancing over corpses.

When President Obama announced the killing of Osama bin Laden on the evening of May 1, he said something which I found so striking at the time and still do: “tonight, we are once again reminded that America can do whatever we set our mind to. That is the story of our history.” That sentiment of national pride had in the past been triggered by putting a man on the moon, or discovering cures for diseases, or creating technology that improved the lives of millions, or transforming the Great Depression into a thriving middle class, or correcting America’s own entrenched injustices. Yet here was President Obama proclaiming that what should now cause us to be “reminded” of our national greatness was our ability to hunt someone down, pump bullets into his skull, and then dump his corpse into the ocean. And indeed, outside the White House and elsewhere, hordes of Americans were soon raucously celebrating the killing with “USA! USA!” chants as though their sports team had just won a major championship.

How Can You Tell if a Hot Dog is Really Made of Beef?

2010-04-28T16:42:00.006-04:00

(Answer: Trust. You have to trust the person who slapped the label on the hot dog.)

Madsen, Kreesten M. et al. 2002. "A population-based study of measles, mumps, and rubella vaccination and autism." New England Journal of Medicine 347: 1480-1482.

This paper by Madsen et. al. is perhaps one of the most widely cited as "scientific proof" there is no link between the MMR vaccine and autism. On the face of it, the study appears to be a straightforward comparison of autism incidence rates between those who had received the MMR and those who hadn't. The authors studied half a million children born between 1991 and 1998 in Denmark. They identified MMR vaccination status (MMR or no MMR) and followed these children to see how many in each group were diagnosed with autism disorders. So far, so good.

The problem is that all risk calculations were done using person-years (persons x years) rather than with the number of children in two groups and how many of each group got autism.

Here were their "rules" for sorting person-years into the vaccinated vs. unvaccinated group.

1. Children were counted in the unvaccinated person-years group until they were vaccinated. Both vaccinated and unvaccinated person-years came from some of the same children.
2. Early-diagnosis (and likely severe, congenital) autism cases were assigned to the unvaccinated group because they were diagnosed prior to vaccination.

So imagine this.

Jack: MMR at age 2. Diagnosed age 4. Followed until age 6.
Unvaccinated group: 2 person-years.
Vaccinated group: 4 person-years.
1 case of autism in vaccinated group.

Bob: MMR at age 2. Followed until age 6.
Unvaccinated group: 2 person-years.
Vaccinated group: 4 person-years.
0 case of autism in vaccinated group.

Bill: MMR at age 2. Followed until age 6.
Unvaccinated group: 2 person-years.
Vaccinated group: 4 person-years.
0 case of autism in vaccinated group.

Ben: MMR at age 2. Followed until age 6.
Unvaccinated group: 2 person-years.
Vaccinated group: 4 person-years.
0 case of autism in vaccinated group.

Henry: Diagnosed age 1. MMR at age 2. Followed until age 4.
Unvaccinated group: 4 person-years.
Vaccinated group: 0 person-years.
1 case of autism in unvaccinated group.

Charlie: No MMR. Followed until age 4.
Unvaccinated group: 4 person-years.
Vaccinated group: 0 person-years.
0 case of autism in unvaccinated group.

Unvaccinated group = 16 person-years.

Vaccinated group = 16 person-years.

One case of autism in each group. No difference between the two groups.

In reality, the above scenario should show 2 cases of autism in 5 vaccinated children vs no case of autism in 1 unvaccinated child. If early diagnosis of autism muddies the vaccinated picture, those cases should have been been pulled from the larger 2 groups and analyzed separately. It would have provided a useful baseline of severe congenital cases to compare against later-onset cases that could possibly be triggered by the MMR.

So, taking Henry of the scenario, there would be 1 case of autism amongst 4 vaccinated children vs. no case of autism in 1 unvaccinated child. Notice how different this picture looks as opposed to the comparison of person-years.

Please note I am not saying that the authors manipulated data until both groups were even. The imaginary scenario simply demonstrates that the use of person-years is NOT the same thing as a straightforward comparison of incidence in both groups. It provides a very distorted picture that does not reflect reality as we know it, which is very malleable to offer whatever kind of results we want, simply based on how long each child is followed.

Other concerns include:

1. Vaccination status of children born before 1996 was inferred from a secondary database. How accurate was this inference method and its resulting data on vaccination status?

2. Many subjects were too young to be diagnosed with autism when the study ended. How much effect did this have on true autism rates in either group?

3. A LOT of calculations were "adjusted." We don't know how. That should always be a red flag, when we are asked to trust that the authors manipulated the data with integrity.

4. How did they diagnose autism? We already know they didn't distinguish between congenital (and likely severe) autism from milder forms or from regressive autism. When investigating whether MMR could be a factor in autism, severity and type of onset should be carefully defined.

5. The study did not exclude the use of other vaccines. Even if the person-years comparison was valid (in that it reflected reality well), the best one can say is that the MMR is not more likely to cause autism than other vaccines, as opposed to being truly "unvaccinated."

The bottom line is, instead of presenting the straight data so we could judge for ourselves how many cases of autism were found in each group, the authors chose to present data that has been processed and diluted and manipulated beyond recognition. Instead of serving us what could have been a sirloin steak of science, we got a hot dog that claims to be made of beef--but in the end, we can't be sure. We just have to take the authors' word for it.

And whenever authors present unrecognizable data with a lot of question marks and ask you to trust them, don't. It's bad science.

Thank God for Independent Journalism

2010-03-11T17:39:00.003-05:00

In today's article, journalist Connie Howard writes a story so simple and so straightforward it boggles the mind why it hadn't been written yet.

On February 2, 2010, the medical journal Lancet retracted their publication of a 1998 paper authored by Andrew Wakefield, MD. If you read major news coverage about this controversy, you would think Wakefield was the devil himself, a shamelessly greedy excuse of a man who used children unethically for personal financial gains. You would also note that in none of these articles did they give Dr. Wakefield a chance to defend himself. In none of the articles did anyone question potential conflicts of interest of those who are condemning him. No, it has been a vilify-Wakefield-fest from start to finish.

Large media conglomerates often do that. They jump on a hatred wagon, usually driven by the elite and powerful, and go hunting with their pitchforks and torches. I don't know what they are teaching journalism majors in school nowadays, but there is as much journalistic integrity in burning someone at the stake as there is in commercials and advertisements.

All I can say is, WOW. Thank you, Ms. Connie Howard, for telling the other side of the story, no matter how unpopular. Thank you, Vue Weekly, for having the courage to publish it.

Obedience = Protection

2010-03-07T10:23:00.011-05:00

The following article is a critique of this paper:
Glanz, JM, et al. Parental Refusal of Pertussis Vaccination Is Associated With an Increased Risk of Pertussis Infection in Children. PEDIATRICS Vol. 123 No. 6 June 2009, pp. 1446-1451

So this guy named Glanz (and his buddies) looked into the database of an HMO and found 156 lab confirmed pediatric cases of pertussis, which also had clear vaccination records, in a 10 year period. Then he randomly picked 595 kids who didn't get pertussis diagnoses, matching the same age and gender of the lab confirmed pertussis kids, to serve as comparison cases.

Out of the 156 pertussis cases, he found 18 of these kids (11%) had parents who refused the pertussis vaccine. The rest, 138 of the kids (89%) had parents who accepted vaccines--their children either got the vaccine or had medical exemptions to miss one or more doses.

Now get this. What he studied wasn't whether or not the kids were vaccinated. There were unvaccinated kids in both the acceptor group and the refusal group! What Glanz was really after was whether parents were accepting or refusing of physician vaccination recommendations. Were they obedient or not? Did they believe in vaccines or not?

Here is my problem. Did the authors really think Bordetella pertussis cares WHY a kid is vaccinated or unvaccinated? Why draw a relationship between parental beliefs and a disease, regardless of vaccination status? They might as well have studied parental religion and pertussis, or parental belief in astrology and pertussis.

See, if it were me, and I were interested in the effects of vaccination on the risk of pertussis, I would group the kids by the number of shots they had, period. Fully vaccinated in one group, partially in the second group, and unvaccinated in the third. I would assume the bacteria do not care WHY kids were unvaccinated or partially vaccinated, forget whether the parents had a bad attitude or not, and focus on the kids' actual biology.

Let's go back to the 595 comparison cases for a moment. They found that while a full 11% of lab confirmed pertussis cases were vaccine refusers, only half a percent (0.5%) of the 595 kids without pertussis diagnoses were vaccine refusers.

So the Glanz guy says, "Hey, look! Most of the vaccine refusers we found are in the group who got pertussis! Kids with rebel, vaccine-refusing parents are 23 times more likely to get pertussis than kids with nice, obedient parents who either vaccinated or didn't vaccinate as they were told to by their doctors."

Breaking medical news! A parent's vaccine acceptance and compliance appears to extend magic protection over a child, regardless of actual immunity. Glanz has discovered a bacterial Passover.

(Eye rolling.) Nuff said, right?

Actually, I got more.

Glanz starts with a group of pertussis kids to find out whether their parents were vaccine acceptors or refusers. Fair enough. But when it came to conclusion time, he reverses the relationship and uses vaccine refusal to predict the risk of pertussis.

Uh uh. You don't do that. At best, it is sloppy. At worst, it is disingenuous. Either way, the reversal is unscientific and makes the conclusion invalid.

If he wanted to use vaccine refusal to predict the risk of pertussis, he should have started with finding all the vaccine refusers in the HMO database. Next, he can look for how many lab confirmed pertussis cases are amongst all the vaccine refusers vs. the randomly picked vaccine accepting controls. Then he can calculate the chances that given a refuser, he would also find lab confirmed pertussis in their charts.

Had he used this method, he might have found very different results. Why?

Not all refusers are exposed to pertussis, for one. What if all the pertussis cases occurred in one school, and that school happened to be in a neo-hippie community with an exceptionally high number of vaccine refusers? What if he took all the comparison cases with no diagnosed pertussis from different schools where pertussis was not going around and happened to have very few refusers.

(He could have matched the control group on age, gender, AND school attended. But he didn't. You would think exposure would be an important variable to control for, but this is the same guy who thinks bacteria care about doctor's notes. So…eye rolling again.)

Now if he had started with all the refusers, you could follow all of them, exposed or not. Match controls on age, gender, and school, and you can have a better idea if refusers truly have a higher risk than acceptors of having lab-confirmed pertussis. At least the conclusion wouldn't be completely incongruent with the study design.

As it stands, the only thing you can possibly conclude from this study is that given a child with lab confirmed pertussis, you are more likely to find that his parents were vaccine refusers than if you were given a child with no diagnosed pertussis (but possibly with asymptomatic pertussis).

First, note that this increased likeliness exists ONLY when compared to a child with no diagnosed pertussis. If you are just looking at the pertussis kids alone, remember that 89% were vaccine acceptors and only 11% were vaccine refusers. That means, given a child with lab confirmed pertussis, you are eight times more likely to find vaccine accepting parents than vaccine refusing parents.

Second, note that I keep saying "lab confirmed pertussis," and not just, "pertussis" like Glanz liked to say. Glanz himself identified three groups of pertussis infections: 1) "frequent asymptomatic infections" where people have pertussis, but show no symptoms; 2) symptomatic infection with no lab confirmation, and 3) symptomatic infection with lab confirmation. Now out of all three, he studied only the third type. Yet in his conclusion, he predicted the risk of pertussis infections in general. Science is supposed to be precise to avoid misleading people. It may sound like nitpicking, but imprecision and overgeneralization is very bad science.

Once you focus on lab confirmed pertussis, and not all pertussis infections, it becomes obvious that diagnostic bias could very well explain the difference between the group with lab confirmed pertussis and the group without diagnosed pertussis. It could be that physicians are more likely to order lab testing for children of vaccine refusers than children of vaccine acceptors. Indeed, Glanz acknowledged this bias.

But get this. He says this bias is roughly cancelled out by another bias: that vaccine refusers are less likely to attend clinics to begin with. He estimates that diagnostic bias is about threefold (3X), and clinic attendance bias was twofold (2x), so the biases cancel each other out and are "negligible." That is to say, doctors are 3 times more likely to order labwork on refusers than acceptors, but refusers are 2 times less likely than acceptors to see that doctor to begin with. So it's all okay.

How did he get this threefold vs twofold estimate? He did a little side analysis of very young children with vague symptoms, counting how many refusers vs acceptors went to a respiratory clinic, and how many refusers vs. acceptors got lab testing orders at the clinic.

Say what?

Very young children with vague symptoms do not represent the initial group, which had mostly older children and were unlikely to have only vague symptoms. It could be that in very young children with vague symptoms, the biases are 3X vs. 2X, but in older children with marked symptoms, the biases could be 5x vs. same, or same vs. 5x, which doesn't cancel out at all. Maybe refusers are more likely to skip clinics for vague symptoms, but if the symptoms are severe, they are just as likely as accepts to go. Maybe doctors are more likely to order labs for refusers for vague symptoms, but when the symptoms are severe, doctors are equally likely to order labs for both groups. We don't know. Again, he is generalizing without the data to support it. Bad, bad science. Since this side analysis of his doesn't represent the core sample of his study, it really is completely useless.

So we're back to the problem of diagnostic bias. It is just one of many possible factors that could explain why if given a child with lab confirmed pertussis, you are likely to find a vaccine refusing parent than given a child without lab confirmed pertussis. You could have doctors who like to order more labwork for refusers, you have happen to have more refuser kids at a school with a pertussis outbreak, you could have refuser kids more likely to exhibit stronger symptoms of pertussis than acceptor kids (who are either vaccinated or have other medical problems for medical exemptions), you could have refusers who are more likely to attend clinics because of stronger symptoms, and so on.

One thing is certain. What you cannot conclude is what Glanz concluded: "Our study found a strong association between parental vaccine refusal and the risk of pertussis infection in children." The study design does not support this conclusion. The methodology doesn't support this conclusion. The data doesn't support this conclusion. It's a case of find one thing, and generalize improperly to make it look like another. It's another case of pseudo-scientific sleight of hand. You got a waste basket for junk science? Crumple this one up and throw it in.

The Red Light District in Science

2010-01-20T14:27:00.004-05:00

Tomorrow's issue of Nature talks about a need for strategic communication about global warming: how to address "gaps" without undermining the understanding.

Climate science, like any active field of research, has some major gaps in understanding (see page 284). Yet the political stakes have grown so high in this field, and the public discourse has become so heated, that climate researchers find it hard to talk openly about those gaps.

There you have it: political stakes. When did science start having political stakes? Show me a science with political stakes, and I'll show you the world's oldest profession in a lab coat.

You know, I have heard this sort of question before. How do we debate honestly without giving the "other side" ammunition for attack? I've heard this in both religious communities and political parties, even in medical circles. But I have never heard this kind of question in a science.

Science is about the search for objective, empirical evidence. It's about letting the facts fall where they may. It's about the search for truth, not the careful and strategic protection of a belief. There is no "other side" in science; there is only an "other side" in politics, ego, careers, money, and legislation. Scientists are not afraid of attacks. It is understood that if a scientific perspective or conclusion is not defensible, it deserves to fall. Only scientists who are more invested in a political ideology than they are in the scientific method are afraid of attacks.

Shame on Nature for not knowing that. Or forgetting it.

ClimateGate

2009-11-24T10:12:00.005-05:00

Climate Audit

Wall Street Journal

New York Times

Washington Post

The Telegraph

Wikipedia with a lot more links.

The links speak for themselves.

Is there antifreeze in vaccines or not?

2009-09-24T01:54:00.005-04:00

In "Toxic Myths About Vaccines," author David Gorski MD accuses anti-vaccinationists of outright lying about toxins in vaccines. He especially ridicules them for being "chemistry-challenged" on assertions regarding one particular toxin: antifreeze.

Here’s one example. The aforementioned Jenny McCarthy has been repeating that there is “antifreeze” in vaccines, as she did in the interview linked to earlier. That line is straight off of a number of antivaccination websites. (Amazingly Mr. Heckenlively managed to restrain himself from repeating “the “antifreeze in vaccines” gambit. I can only hope that it is due to intellectual honesty, although I can’t rule out the possibility that he just didn’t know about it.) One website in particular links to an MSDS about Quaker State Antifreeze/Coolant, the principal ingredients of which are ethylene glycol and diethylene glycol. Guess what? There’s no ethylene or diethylene glycol in vaccines.

Not so fast, Dr. Gorski. There IS ethylene glycol in vaccines. It's called 2-Phenoxyethanol, and is found in childhood vaccines Infanrix, Deptacel, Pediarix, and Ipol, amongst others. You see, the other name for 2-Phenosyethanol is ETHYLENE GLYCOL monophenyl ether.

The MSDS on car antifreeze, the regular ethylene glycol, says that the lethal oral dose to kill 50% of rats is 4700 mg/kg. The MSDS on 2-Phenoxyethanol, the vaccine ethylene glycol, says the lethal oral dose to kill 50% of rats is 1260 mg/kg. Comparing apples to apples, the vaccine ethylene glycol is a lot more toxic than car antifreeze--to rats anyway.

The debate shouldn't be on whether ethylene glycol exists in vaccines. It does, period. The debate should be on whether this type of ethylene glycol and this amount of ethylene glycol can cause the same adverse reactions as those normally associated with car antifreeze.

It is a situation where both sides are bending and polarizing the truth to suit their own agendas, while parents looking for honest, straightforward, objective information are screwed. Is antifreeze in vaccines? Not exactly--not the kind we put in our cars. Aha, then antifreeze is NOT in vaccines? Not exactly--a type of ethylene glycol that is known to have similar (actually higher) levels of toxicity to car antifreeze is found in very small amounts in a number of childhood vaccines.

So word to the wise, parents. Do your own research. How do you sort it out, when both sides are liberal with the truth-bending?

1. Look for precision. Science is precise. It is not whether A is true or not true. Science defines A carefully, and then qualifies under what conditions A is true and not true. Anyone who gives you a simple "fact" is bending the truth, because reality is not simple.

2. Look for references. Someone says there is antifreeze in vaccines? What makes them say that? Someone says it is NOT in vaccines? Where all have they looked? Follow their research trail for arriving at that conclusion. (In this case, if they had looked under the right chemical names, they would have found it.)

3. Look for objectivity. Read the original research papers. Outline the "plot"--what did they do in the study? Now to tease out confirmation bias, blind yourself to the results. Switch the research findings so that the results come out the opposite of what you would like to believe. If the study finds no autism-vaccine connection, much to your relief, then pretend it did. If the study finds a strong autism-vaccine connection, as you knew it would, pretend it didn't find anything at all. Once the results are disagreeable, the flaws in the research design and methodology come leaping out like magic.

4. Trust no one but yourself. If you let other people do the thinking for you, then you'll just end up with other people's thoughts--and prejudices, and agendas. It's kind of obvious, but it needs to be said. This is what this blog is all about: think for yourself.

For further research:
Vaccine excipient table sorted by vaccine.

Hasty Generalizations

2009-06-09T10:47:00.002-04:00

There is a very common fallacy called, "hasty generalization." Basically, it says it is illogical to assume something happens all the time, everywhere, just because you've seen it once, or a few times.

The fallacy is illustrated in this common joke.

An engineer, a scientist, and a mathematician were riding a train into Ireland. As they observed the passing vista, they saw a black sheep. The engineer commented, "Interesting, that the sheep in Ireland are black." The scientist corrected him, "Let's not generalize too hastily. At least one sheep in Ireland is black." The mathematician thoughtfully added, "No, at least one side of one sheep in Ireland is black."

We all generalize hastily without knowing that we are. But in some cases, the fallacy is rather obvious. Take the case of global warming. Physicist Freeman Dyson recently commented on generalizations, not only from regional warming, but also from inadequate computer models that do not represent the true dynamics of the planet.

Freeman Dyson Takes on the Climate Establishment

"Global Warming is Baloney"

2009-06-09T10:28:00.003-04:00

Tennesee Burger King Signs, "Global Warming is Baloney."

Free speech in the world of contracts. Does a franchise have the right to free speech when it embarrasses the brand name?

I went to sleep in America....

2009-06-07T05:20:00.005-04:00

Recently, John Cusack wrote an article criticising the direction of the Obama administration. He quoted Constitutional law professor Jonathan Turley:

"Well it can't get any worse: extreme executive privilege arguments in court, withholding of abuse photos, adoptions of indefinite detentions without trial, restarting military commissions, and blocking any torture investigation. Welcome to Bush 2.0..."

That is a nice summary of decision after decision coming out of our current administration that has anyone who cares about the Constitution concerned, to say the least. Let's recap:

1. Sovereign immunity (April 09): The government can spy on you as much they want, as long as they don't tell the public what they got on you.

2. Secrecy law (May 09): If the government did anything illegal between 9/11/01 and 1/22/09, they don't have to tell you, or show anyone the pictures they took.

3. Indefinite preventative detentions (May 21 09): If the government thinks you're dangerous, they can lock you up. Without a trial. Forever.

Now we have the latest:

4. Proposal to allow guilty pleas in capital cases--without a trial. If the government wants to sentence you to death, they can torture you--excuse me, interrogate you intensely--until you confess, and execute you based on that confession.

One comment I read in response to Glen Greenwald's blog on this topic inspired the title to my own blog: "I went to bed in America and woke up in a soviet gulag."

I have one minor disagreement. I think we went to sleep in America, and none of us has woken up yet.

What is Pseudoscience?

2009-05-20T22:25:00.013-04:00

The real purpose of the scientific method is to make sure Nature hasn't misled you into thinking you know something you don't actually know.
-- Robert M. Pirsig, Zen and the Art of Motorcycle Maintenance

When I googled "pseudoscience," I got a lot of pages describing pseudoscience as any group of ideas that is not accepted by the establishment and is not consistent with the established body of knowledge. In other words, most people use the word "pseudoscience" to name-call and disparage claims they do not believe and do not like. Sure, they talked a lot about how pseudoscience is not testable, but they do not define testability.

After all, there are different standards for tests. What is "testing" to one person is sloppy, inconclusive blather to another. In science, the quality of the data lies in how well others can independently "test" their validity and reproduce the same results. The more details provided to evaluate and reproduce the methodology, the higher the quality of the paper. The more you have to trust the authors' word for anything, the lower the quality is. Requiring trust, or faith, in the methodology is not found in real science.

This motivated me to write my own page on how to identify pseudoscience. Rather than use the label to discredit non-conventional thinking, I would list testability criteria that can apply to popularly accepted knowledge as well. In addition, I would emphasize that there is no specific cut-off in the pseudo-science/science spectrum. Rather, it is important to understand that the less scientifically rigorous a conclusion is, the more pseudoscientific it is.

How to Recognize Pseudoscience:

The bottom line in identifying pseudoscience is recognizing claims and conclusions that are not supported by the evidence provided. Just like commercial products, evidence comes in varying qualities. Pseudoscience is the infomercial or used-car-salesmanship of the science world, a lot of exaggerated, selective conclusions not supported by the quality of the product (or data).

Now all scientific papers have flaws. It is not that we expect perfection in the data. We just don't want any false advertising, if you will, that ignores the flaws.

As an example, I will use a study frequently cited by government and health authorities as further "scientific" evidence that thimerosal (mercury) in vaccines do not cause autism. In 2003, Madsen et al conducted a retrospective study looking at autism rates in Denmark before and after the removal of thimerosal in vaccines, from 1971 to 2000. After the removal of thimerosal in 1992, autism rates actually skyrocketed. Authors concluded that thimerosal cannot be a causal factor in the development of autism. (Reference: Madsen, MD et all. PEDIATRICS Vol. 112 No. 3 September 2003, pp. 604-606 Thimerosal and the Occurrence of Autism: Negative Ecological Evidence From Danish Population-Based Data.)

I chose a paper popularly cited by authorities to show that my pseudoscience criteria are not biased against novel ideas, but can apply to conventionally accepted claims as well. Real science is not attached to an ideological agenda either pro or against establishment, but calls out the flaws where they occur, and demands that conclusions match the data.

1. Vague definitions / Lack of transparency and critical details: Pseudoscience does not use precise, objective, and transparent definitions that can be independently evaluated for as valid. Their definitions are often lacking in detail and murky, forcing the reader to trust that the authors really measured what you think they measured.

For example, in Madsen et al's paper, the authors defined autism as meeting criteria for ICD-8 code 299, "psychosis proto-infantilis." Why did they not use ICD-8 code 308, "infantile autism," like everyone else? When I emailed the lead author for clarification, he simply replied that Denmark had always used ICD-8 code 299 for autism, skipped the use of ICD-9 altogether, and for the specific diagnostic criteria, I would have to consult a Danish child psychiatrist. In short, before 1994, for 23 out of the 29 years in the study period, we have no clear definition of autism. Since Denmark departed from ICD-8 codes in other countries, the specific diagnostic criteria they used is critical. As it stands, we simply have to trust that what the Danes saw as "psychosis proto-infantilis" is the same animal we see as "autism."

2. Changing definitions. Pseudoscience lumps inconsistently defined measurements all together. At best, it is sloppy and unreliable. At worst, it constitutes a sleight-of-hand. Imagine someone tells you that Chemical A caused a study subject to lose weight. But you find out that the "before" weight measurement was taken on a different scale than the "after" measurement. In real science, one would use exactly the same scale for an honest comparison.

Denmark removed thimerosal in 1992. In 1994, Denmark changed its diagnosis of autism from ICD-8 299 "psychosis proto-infantilis" to ICD-10 F84 "infantile autism." Before 1995, Denmark's autism rates counted only inpatient autism cases, those severe enough to be admitted for hospitalization. After 1995, Denmark started counting both hospitalized cases and outpatient cases. So autism rates skyrocketed after thimerosal was removed. Did they really skyrocket, or did it look like that because the definition of autism changed to count a lot more kids? We will never know. The data don't say.

3. No real or actual data provided / Insufficient or adjusted statistics. Pseudoscience present data in graphs or some statistical artifact such as person-years or relative risk. They only show "adjusted" data, even when a straight-up presentation of raw data will do. Then they omit details that would allow independent evaluation of how the data was "adjusted" and if that adjustment was valid.

In Madsen et al's paper, we only see a graph of incidence rates from 1971 to 2000. Any 5th grader who has watched PBS's Cyberchase can tell you that graphs can imply statistical significance, or a sharp rise or fall, where there is actually none. Was the rise significant or just a random fluctuation? The data don't say.

4. No control group / Poor control group. Pseudoscience infers causation from a change in one group, without comparing it to another group that hasn't experienced the change. In science, the comparison group is called the control group. Pseudoscience often has no control group, or designs a control group that is so different from the study group that you can't pinpoint what caused any difference in results.

In the study in question, there simply was no control group. As a correlational study, it did not involve experimentation, and therefore did not strictly follow the scientific method. All correlational and anecdotal studies are quasi- or pseudo-scientific right out of the gates, and need to be interpreted with many qualifiers and exceeding caution.

As it was, it would have been helpful to compare autism rates with those in other countries using similar diagnostic criteria, or compare autism rates between different groups in Denmark, such as autism rates between vaccinated children and unvaccinated children never exposed to thimerosal. Without any comparison at all, the study is not much more than a glorified anecdote of one country instead of one person. Without any controls, anecdotes of thousands of people are still anecdotes.

5. No or inadequate analysis of confounders. In science, confounders are factors that could have caused the result you see instead of, or in addition to, the factor you are studying (called the independent variable). Pseudoscience doesn't consider the impact of factors other than the independent variable.

To illustrate, in the Danish study, a possible confounder is that the amount of thimerosal Danes were exposed to was very low compared to that in other countries. It is possible that the amount of thimerosal is critical in causing autism or not, the Danes were not affected by the low amount they got. Another possible confounder is that thimerosal is only one of several co-factors that play a role in causing autism, or one of many different causes. After all, the autism spectrum disorder has many clinical presentations, and it is entirely possible it has multiple and complicated etiologies. Just because autism rates rose despite absence of thimerosal doesn't automatically mean thimerosal does not play a role. Just because obesity rates rise despite a shortage of ice cream doesn't mean ice cream doesn't make you fat.

6. No or inadequate analysis of flaws and weaknesses. Pseudoscience doesn't have flaws or holes, or doesn't acknowledge as many as it should. Real science is self-critical to carefully place necessary limitations and qualifiers in its conclusions.

Although Madsen et al acknowledged that changing the definition of autism to include outpatient cases after 1994 might have inflated the rates somewhat, the authors downplay the impact. They do not address the other vagaries of definition at all, nor any of the flaws listed here. They do not qualify or limit their conclusions in context of these flaws.

7. Conclusions unwarranted by the data. Pseudoscience likes to jump to conclusions. Real science is painfully slow, painfully tentative, and painfully precise about interpretation.

The only conclusion warranted by this study is that between 1994 and 2000, inpatient diagnoses of ICD-10 F84 childhood autism appear to have risen in Denmark despite no use of thimerosal in childhood vaccines, but it is unknown if this "rise" is significant or a result of random fluctuation. Certainly, no conclusions on causation or exoneration from causation can be inferred.

If you look at the data in this study against the rigorous standards of real science, they are too vague and inconsistent to support the conclusion that thimerosal cannot possibly cause autism. The study has all the outward trappings of "scientific" research, but none of the fundamental pillars of real science. It is the poster-child of pseudoscience.

Summary

Whenever you read a paper where you don't have enough information to independently evaluate and "test" the data and statistics for validity, a red flag should go up. Pseudoscience can be found everywhere, from widely accepted research findings supported by authorities to "alternative"/paranormal/conspiracy theories.

Blog on hiatus

2009-02-04T10:20:00.003-05:00

I regret to announce that this fledgling blog is on hiatus from December 2008 to May 2009. I recently moved to an apartment in South America that has no phone or internet access. Despite my best intentions, I cannot do the kind of research necessary for blogging without continuous access to the internet. I plan to be back when I can.

Will the Real CO2 Please Stand Up?

2008-11-22T10:57:00.025-05:00

Question: Can carbon dioxide levels from any *one* source be considered representative of the levels of the entire planet?

Ice core vs. Surface measurements

While I was investigating whether the planet was warming, I had always assumed CO2 levels were unquestionably rising. After all, we've all seen graphs like this:

(IPCC: Ice core proxy levels followed by direct measurements)

Since some "adjusting" is done to correlate CO2 amounts in air bubbles to the amount thought to be in the atmosphere at that time, ice core measurements are not as good as the real thing, but are thought to be valid proxies for direct measurements.

Then last year (2007), a German researcher named Ernst Beck published another graph, made from direct CO2 measurements, that looks like this:

http://www.biomind.de/realCO2/

(CO2 levels are the red line.)

He showed a peak back in the 1820's near 400 ppm, which throws the entire temperature-CO2 correlation out of whack. Not only that, he accused authors of conventional graphs of cherry-picking data that suited their ideological agenda, of "falsifying the history of CO2." Both Beck and the journal that published the study (Energy and Environment) were immediately attacked by global warming proponents as, to put it politely, unworthy of publishing. Keeling, whose work is the cornerstone of the IPCC graph, calls Energy and Environment as a "forum for laundering pseudo-science."

Name-calling aside, is there any validity to Beck's data on CO2? He compiled 90,000 chemical measurements of CO2 from "180 technical papers published between 1812 and 1961."

"The compilation of data was selective. Nearly all of the air sample measurements that I used were originally obtained from rural areas or the periphery of towns, under comparable conditions of a height of approx. 2 m above ground at a site distant from potential industrial or military contamination...

...Discounting such unsatisfactory data [because of deficiencies in certain methods], in every decade since 1857 we can still identify several measurement series that contain hundreds of precise, continuous data."

His critics, including Keeling, claim that these measurements are neither here nor there. They have too much variability and do not represent the true "background" level of CO2. The only reliable source of this true, "background" CO2 for that time period is in air bubbles trapped in antarctic ice. Everything else is just irrelevant noise. Note that they are not disputing the accuracy of the data. They are saying anything with that much variance is unacceptable.

It seems to me that 90,000 readings in 180 published papers should not be so easily dismissed. If nothing else, they show that measured CO2 levels had a huge amount of variance. Yet none of this variance is taken into consideration because all but one source of CO2 measurements (ice core proxies) are categorically rejected.

Now Beck admits that not all CO2 data is equal. He himself threw out data he felt was not representative because of faulty methodology. But who decides what is faulty? Who decides what is representative of the CO2 level in our atmosphere? How does one decide that one measurement (ice core) is representative, and the other (chemical readings near the surface) is not? Who gets to define "background level"? Of all the CO2 measurements out there, is the *one* source selected to represent the planet a matter of consensus as well? A vote by a panel of judges, like a beauty pageant? Is this how science is conducted now?

In science, one has to have a serious and evidence-supported justification for ignoring data. Whenever empirical data is rejected, it is a red flag. Without judging the rationales given for purposely excluding data, both the IPCC and Beck are waving it. Of course, the IPCC rejected a hell of a lot more data (90,000 direct measurements), so one could say their red flag is overwhelmingly larger than Beck's. The more data you reject, the better your reason for rejection must be.

Drive your data, change your definition

Speaking of red flags, whenever a graph changes its definitions mid-point, alarms should sound loud and strong. This is especially true if the methodology changes at a pivotal point in the graph. For example, Jawarowski, a vocal critic of ice core proxies, highlights this change in this graph.

(From: Jaworowski, Z., 2007, CO₂: The greatest scientific scandal of our time, EIR Science)

Notice how after they change the definition of CO2 levels from ice core readings to actual measurements from CO2 stations, the curve rises exponentially. Yeah, that should turn on the ambulance sirens in any scientist's head. You can make the "trend" go in any direction you want simply by changing to a different set of data. It could be a defensible change. It could also be sleight of hand.

I understand using proxies because CO2 measuring stations did not exist back then. But why use proxies when there were direct CO2 measurements during the same time period? Wouldn't direct CO2 measurements back then be more comparable to direct CO2 measurements taken today than proxies? Do they have a *really* good reason for rejecting all that data?

What is "background" CO2 anyway?

Keeling, quoting his father's pioneering work on "background" CO2, explains:

"Measurements of the concentration of atmospheric carbon dioxide extend over a period of more than a hundred years. It is characteristic of all the published data that the concentration is not constant even for locations well removed from local sources or acceptors of carbon dioxide. Recent extensive measurements over Scandinavia, reported currently in Tellus, emphasize this variability: observations vary from 280 to 380 parts per million of air. These measurements are in sharp contrast to those obtained in the present study. The total variations at desert and mountain stations near the Pacific coast of North America, 309 to 320 parts per million is nearly an order of magnitude less than for the Scandinavian data. The author is inclined to believe that this small variation is characteristic of a large portion of the earth's atmosphere, since it is relatively easier to explain the large variations in the Scandinavian data as being a result of local or regional factors than to explain in that way the uniformity over more than a thousand miles of latitude and a span of nearly a year, which has been observed near the Pacific coast."

In other words, "background" CO2 is whatever source that has the least amount of variance of CO2. Why? Because the author is "inclined to believe" the smallest variation is representative of the earth's atmosphere. His definition of "background" is not based on actual atmospheric measurements showing it has very little variance. No, it is because it makes sense to him the background shouldn't vary all that much.

Keeling continues to say:

"The concept of the atmospheric background has been backed up by millions of measurements made by a community of hundreds of researchers."

But he has no references for these millions of measurements (though he references other assertions in his critique). So I can't independently verify what he means by that. If the "background" definition has empirical support, this empirical data should be foremost in his argument. As it stands, it sounds like atmospheric background is a concept, widely accepted to be sure, but not very well defended. And in science, accepted and defended are two different things.

Incidentally, there are only five major CO2 measuring stations (atmospheric baseline observatories). Most of the data for mean monthly or annual CO2 levels come from the station on an active volcano (Earth's largest) called Mauna Loa, which last erupted in 1984. I assume climatologists have taken into account volcanic gases (one of which is CO2) as a potential confounder, and that this has nothing to do with the much higher readings of CO2 since they started taking direct measurements there.

Conclusion

So they rejected a huge amount of empirical data with a lot of variance for a proxy that has very little variance, barely climbing for centuries. Then they attached actual measurements, and CO2 levels leap. How much of it is an artifact of data exclusion and definition change?

I don't know the answer. But I shouldn't have had to ask the question.

A Stand-Up Statistician

2008-11-20T13:08:00.006-05:00

Who knew statisticians could be humorous?

I've just become a fan of William M. Briggs, statistician blogger. From global warming to current events, Dr. Briggs turns a painfully dull subject (for the rest of us) into entertaining lessons on the limits and pitfalls of statistics.

Take this for example.

September 6, 2008: Do not smooth time series, you hockey puck!

Dr. Briggs comments:
"The various black lines are the actual data! The red-line is a 10-year running mean smoother! I will call the black data the real data, and I will call the smoothed data the fictional data. Mann used a “low pass filter” different than the running mean to produce his fictional data, but a smoother is a smoother and what I’m about to say changes not one whit depending on what smoother you use.

Now I’m going to tell you the great truth of time series analysis. Ready? Unless the data is measured with error, you never, ever, for no reason, under no threat, SMOOTH the series! And if for some bizarre reason you do smooth it, you absolutely on pain of death do NOT use the smoothed series as input for other analyses! If the data is measured with error, you might attempt to model it (which means smooth it) in an attempt to estimate the measurement error, but even in these rare cases you have to have an outside (the learned word is “exogenous”) estimate of that error, that is, one not based on your current data."

When we normal people holler about this, we look like presumptious malcontents. It's so much funnier when a PhD statistician says it.

This ties into something that's been bugging me. In my search for the "elusive standard deviation" (of the global mean surface temperature), I kept coming across well-meaning folk telling me the standard deviation of the global mean temperature is a fraction of a degree Celsius, usually around 0.25° C. They say there isn't that much variability when you compare the means across the years. It is always going to hover very closely near the climatology.

Then it dawns on me they are treating the means themselves as raw measurements, like readings from a thermometer. Instead of seeing the means as statistical artifacts with a huge amount of uncertainty, they get a clean slate as absolute numbers with no error attached to them. If you take averages of averages of averages, you are going to end up with nice, tidy numbers with no variance at all. Yes, you can serially average, but each step of the series has to propagate the error from all previous averages. If you smooth the time series, over and over again, without propagating the error, you are going to end up with "fictional data" that has almost no variance and is all but certain.

I don't know about you, but I'd rather have "an uncertain truth" than a likely fiction.

Here are some of my favorite Briggs blogs.

November 12. 2008: Arcsine Climate Forecast
October 31, 2008: Breaking the Law of Averages: Probability and Statistics in Plain English
October 12, 2008: Peer Review Not Perfect: Shocking Finding

I'll close with my favorite quotation on statistics:

"There are three kinds of lies: lies, damn lies, and statistics."
-- Benjamin Disraeli, author, British statesman (1804-1881)

The Elusive Standard Deviation

2008-11-19T10:21:00.002-05:00

Question:
What is the standard deviation of the global mean surface temperature, per year?

The issue of global warming begins by asking the question: Is the planet warming? The answer provided by the consensus of climate scientists is a resounding, "Yes." The cardinal statistic supporting this assertion is a rise in the "global mean/average surface temperature." For example:

"Records from land stations and ships indicate that the global mean surface temperature warmed by between 1.0 and 1.7°F since 1850...Since the mid 1970s, the average surface temperature has warmed about 1°F."
http://epa.gov/climatechange/science/recenttc.html
"Over both the last 140 years and 100 years, the best estimate is that the global average surface temperature has increased by 0.6 ± 0.2°C."
http://www.ipcc.ch/ipccreports/tar/vol4/english/075.htm

It stands to reason that any examination of the global warming problem has to begin here, with the "global mean/average surface temperature." We need to know how much this mean has risen, and if this rise is statistically significant.

The means as a probability distribution
Read more here: http://en.wikipedia.org/wiki/Normal_distribution

Suppose you average the age of everyone in your household. You have three people aged 3, 14, and 43. The average (also called the "mean") age is 20 (3+14+43=60, divided by 3). Suppose you average the age of everyone in the house next door. They have 5 people aged 18, 19, 20, 21, 22. The average age of their house is also 20. The same exact average in two households can describe two very different situations. The fact of the matter is, the mean has limits in its ability to represent a group with one single point. One has to put that single point in context of how spread out the data is; in statistics, the spread is called "variance." There is a huge amount of variance in your house, not so much in your neighbor's.

The smaller the variance, the more "truthful" the mean is in representing the population. You don't even have anyone in your household who is twenty years old; this mean does not "truthfully" represent your household. The average age of 20 is a more accurate representation of the population in your neighbor's house than in your house. That is because your neighbor's house has a smaller variance (5 years) than yours (40 years).

The mean, as a single point representing an population, is a probability distribution. It represents the middle of a distribution of data shaped like a bell curve. Statisticians divide each half of the bell curve into roughly three equal sections; each section is called a standard deviation. So there are six standard deviations in a bell curve: three before the mean, and three after the mean. When you go out one standard deviation from the mean, both under and over the mean, you get 68% of the data in a typical bell curve. You have a 68% chance that the "true" mean lies within one standard deviation (SD), a 95% chance that it lies within two SDs, and a 99.7% chance it lies within three SDs (also denoted by σ, lowercase sigma).

When you compare the means of two different bell curves, the question is: what are the chances the difference is caused by random or meaningless fluctuations? The higher the variance of the two distributions, the more likely the "true" mean is uncertain, and the more likely the differences between the two uncertain means are random and not significant. The quickest way to eyeball the significance of any comparison is to see if the difference is more than one SD. If the change is larger than one SD, it is likely to be significant. If the change is smaller than one SD, the mean is likely to be due to random fluctuations and errors in measurement.

Where's the mean? Where's the SD?

Now that we've gotten elementary statistics out of the way, let's look at that mean surface temperature again. The rise in the global mean surface temperature is oft cited and well known. But when you look at the graphs referenced by discussions of mean temperatures, you don't see any numbers for the means. You see a horizontal line labeled "0.00° C" and vertical bars going fractions of a degree below or over the zero. (For an example, look here.) One assumes that the bars represent the global mean temperatures, and they are going up. But upon closer examination, the bars represent not the global mean temperatures, but "global temperature anomalies," or departures from the Big Zero in the middle.

How do you evaluate the mean, if you don't know what the mean is or what its variance looks like? What is the absolute value of the global mean surface temperature, and what is its standard deviation? I was able to find this table showing the absolute mean temperatures per year from 1880 to 2007. In addition, this GISS site describes:

"For the global mean, the most trusted models produce a value of roughly 14 Celsius, i.e. 57.2 F, but it may easily be anywhere between 56 and 58 F and regionally, let alone locally, the situation is even worse."

So we can estimate the global mean to hover around 14° C. But what do these average temperatures represent without knowing their standard deviations?

Here is what I mean. A table that shows this:
1880: 13.88° C ± 10° C
2007: 14.73° C ± 10° C

reads very differently from a table that shows this:
1880: 13.88° C ± 0.25° C
2007: 14.73° C ± 0.25° C

Now given the fact that temperatures across the planet have a huge variance over the year, it is impossible that the standard deviation would be less than 1°C. (That would mean the global temperature distribution roughly ranges from 11° C to 17° C, which we all know is patently false.) What is the likely ballpark of the standard deviation?

The hottest record in Canada is 45° C (113° F) and the coldest record in Africa is -24° C (-11° F). It is not unreasonable to estimate the bulk of the world's temperatures for the year falls roughly in this range. This type of range would give us 30° C below and 30° C above the mean of 14° C, which gives a reasonable estimate that the SD should be in the ballpark of 10° C. (If the spread is actually wider, the SD would be even larger.)

If that is the case, why would a change from 13.88° C ± 10° C to 14.73° C ± 10° C be considered significant? The difference is well within the margin of error. Why would climate scientists make such a big to-do about this fraction of a degree increase, in context of the huge global variance? Why can't we find the exact SD?; surely it has been calculated. (You can google it until the cows come home, but you won't find that SD.)

Who is the Big Zero?

It turns out that there is no such thing as the "global mean surface temperature" or its SD as statistical entities. How then, do they know the mean is rising, if it doesn't exist? What are they comparing the "anomalies" to? Who is this Big Zero in all the graphs and data tables?

I was naive and ignorant enough to assume that climate scientists take temperature readings from weather stations all over the earth for the period of a year, and average them into one temperature. Then they compared that average from year to year to observe a rising trend in this mean temperature. Not.

This is what really happens. Climate scientists take readings from weather stations and feed the data into a computer model that adjusts for all sorts of variables, including number of wet days, cloud cover, sunshine, diurnal temperature range, etc. Using computer modeling, they divide the world into 5x5 grids, and fill the boxes with known data and interpolate unknown data. They run the model for a while and come up with a single mean for a 30 year period, usually 1961 - 1990. This mean is called a climatology. The GISS site discusses the Elusive Absolute SATs (Surfact Air Temperatures):

Q. If SATs cannot be measured, how are SAT maps created ?

A. This can only be done with the help of computer models, the same models that are used to create the daily weather forecasts. We may start out the model with the few observed data that are available and fill in the rest with guesses (also called extrapolations) and then let the model run long enough so that the initial guesses no longer matter, but not too long in order to avoid that the inaccuracies of the model become relevant. This may be done starting from conditions from many years, so that the average (called a 'climatology') hopefully represents a typical map for the particular month or day of the year.

There are differing methods and time periods used for modeling climatologies, resulting in different climatologies. Climate scientists pick the climatology most appropriate for purpose and compare their annual mean temperatures (also calculated by computer models) to it. The climatology is the absolute standard by with all other temperature calculations are measured; it is the Big Zero. All annual temperatures are evaluated in terms of either hotter or colder than the climatology. The climatology itself does not have a standard deviation, because it is not a straightforward average, but a "adjusted" figure, a result of a very educated computer guess. Scientists estimate the margin of error of these climatologies to be exceptionally small. Climate Research Unit (CRU) explains here.

How accurate are the hemispheric and global averages?

Annual values are approximately accurate to +/- 0.05°C (two standard errors) for the period since 1951. They are about four times as uncertain during the 1850s, with the accuracy improving gradually between 1860 and 1950 except for temporary deteriorations during data-sparse, wartime intervals. Estimating accuracy is a far from a trivial task as the individual grid-boxes are not independent of each other and the accuracy of each grid-box time series varies through time (although the variance adjustment has reduced this influence to a large extent). The issue is discussed extensively by Folland et al. (2001a,b) and Jones et al. (1997). Both Folland et al. (2001a,b) references extend discussion to the estimate of accuracy of trends in the global and hemispheric series, including the additional uncertainties related to homogeneity corrections.

Why do climate scientists use climatologies, instead of a straight-up means? The same CRU site elaborates.

Why are the temperatures expressed as anomalies from 1961-90?
Stations on land are at different elevations, and different countries estimate average monthly temperatures using different methods and formulae. To avoid biases that could result from these problems, monthly average temperatures are reduced to anomalies from the period with best coverage (1961-90). For stations to be used, an estimate of the base period average must be calculated. Because many stations do not have complete records for the 1961-90 period several methods have been developed to estimate 1961-90 averages from neighbouring records or using other sources of data. Over the oceans, where observations are generally made from mobile platforms, it is impossible to assemble long series of actual temperatures for fixed points. However it is possible to interpolate historical data to create spatially complete reference climatologies (averages for 1961-90) so that individual observations can be compared with a local normal for the given day of the year.

Why do they talk about the mean surface temperature, if the mean doesn't exist? The best I can make of it, the mean is a theoretical estimate (climatology + the anomaly) that is assumed to be a close correlate of global anomaly trends. If the anomalies go up, it is undisputed that the mean has also gone up.

So is the rise in global mean surface temperature significant?

It depends. Not on objective data, mathematical rigor, and the scientific method; but on personal values. Do you trust climate scientists and their computer modeling or not? It is funny way to do science, because when it comes down to it, it is a matter of belief. Do you believe they have done a good job in "adjusting" all the variables in their computer models? Do you believe they have both the intellectual competence and the professional integrity to have factored in all the relevant variables accurately to get the "truth"?

If you are comparing computer-adjusted data with computer-adjusted data, how do you know if the difference between them is significant? You have to trust the person doing the adjusting.

Please don't get me wrong. I am not casting aspersions on climate scientists at all. I am describing an inherent subjectivity in all endeavors entirely dependent on computer modeling. What comes out is simply a function of what goes in. You program the computer to spit out whatever number you want. And the decision of what goes in is ultimately subjective. Unlike the situation in other sciences where methodology interacts with reality, and you get the results you get whether you like it or not; computers do not interact with the real world. The computer model does not get feedback from reality, only from the programmer. There is no way to cut out the subjective input and values of the programmer from the process.

The only check and balance that exist in a field comprised of entirely computer modeling is the community of scientists and their subjective approval of one's programming. It is no wonder that "consensus" is used so much to describe climate change.

-----------------------------
Links for further reading:

Steve McIntyre's Climate Audit
Mathematician who "audits" climate modeling
20 Questions Statisticians Should Ask About Climate Change (pdf)
by Edward J. Wegman, statistician, George Mason University
William M. Briggs, Statistician: Blogs on Global Warming
Letter to Call for Review of the IPCC
by Vincent Gray, climate scientist and former IPCC Reviewer