Skepticism about science and medicine

In search of disinterested science

Archive for the ‘medical practices’ Category

We are being routinely misled about health and diet

Posted by Henry Bauer on 2017/03/24

Most of what the media make a fuss about over health or diet should not be believed.

It should not be believed even as it cites peer-reviewed articles or official guidelines. All too often the claims made are based on misuse of statistics and are an abuse of common sense.

That little rant was set off by a piece in the august New York Times: “Pollution leads to greater risk of dementia among older women, study says”).

Alarms were triggered:
“Older women”: Only among older and not younger? Women but not men?

The original article did not improve my mood:
The pollution actually studied was “fine particulate matter, P.M. 2.5, 2.5 micrometers or smaller in diameter”: What about 2.5 to 3, say? Or 3 to 4? And so on.
“Women with the genetic variant APOE4, which increases the risk of Alzheimer’s disease, were more likely to be affected by high levels of air pollution”:
Is this asserting that there’s synergy? That the combined effect is not just the added effects of the two factors? That pollution is not just an independent risk factor but somehow is more effective with APOE4 carriers? So what about APOE3 or APOE2 carriers?

The New York Times piece mentioned some other studies as well:
“[P]renatal exposure to air pollution could result in children with greater anxiety, depression and attention-span disorders”.
“[A]ir pollution caused more than 5.5 million premature deaths in 2013”.

With those sort of assertions, my mind asks, “How on earth could that be known?”
What sort of study could possibly show that? What sort of data, and how much of it, would be required to justify those claims?

So, with the older women and dementia, how were the observational or experimental subjects (those exposed to the pollution) distinguished from the necessary controls that were not exposed to pollution? Controls need to be just like the experimental subjects (in age, state of health, economic circumstances, etc.) with the sole exception that the latter were exposed to pollution and the controls were not.
For the controls not to be exposed to the pollution, obviously the two groups must be geographically separate. Then what other possibly pertinent factors differed between those geographic regions? How was each of those factors controlled for?

In other words, what’s involved is not some “simple” comparison of polluted and not polluted; there is a whole set of possibly influential factors that need somehow to be controlled for.

The more factors, the larger the needed number of experimental subjects and controls; and the required number of data points increases much more than linearly with the number of variables. Even just that realization should stimulate much skepticism about many of the media-hyped stories about diet or health. Still more skepticism is called for when the claim has to do with lifestyle, since the data then depend on how the subjects recall and describe how they have behaved.

The dementia article was published in Translational Psychiatry, an open-access journal from the Nature publishing group. The study had enrolled 3647 women aged between 65 and 79. That is clearly too small a number for all possibly relevant factors to have been controlled for. Many details make that more than a suspicion, for example, “Women in the highest PM2.5 quartile (14.34–22.55 μg m −3) were older (aged ≥75 years); more likely to reside in the South/Midwest and use hormonal treatment; but engage less in physical activities and consume less alcohol, relative to counterparts (all P-values <0.05. . . )” — in other words, the highest exposure to pollution was experiences by subjects who differed from controls and from other subjects in several ways besides pollution exposure.

At about the same time as the media were hyping the dementia study, there was also “breaking news” about how eating enough fruit and vegetables protects against death and disease, based on the peer-reviewed article “Fruit and vegetable intake and the risk of cardiovascular disease, total cancer and all-cause mortality — a systematic review and dose-response meta-analysis of prospective studies”.

Meta-analysis means combining different studies, the assumption being that the larger amount of primary data can make conclusions stronger and firmer. However, that requires that each of the individual studies being drawn on is sound and that the subjects and circumstances are reasonably comparable in all the different studies. In this case, 95 studies reported in 142 publications were analyzed. Innumerable factors need to be considered — the specific fruit or vegetable (one cannot presume that apples and pears have the same effect, nor cauliflower and carrots); and the effects of different amounts of what is eaten must somehow be taken into account. There are innumerable variables, in other words, permitting considerable skepticism about the claims that “An estimated 5.6 and 7.8 million premature deaths worldwide in 2013 may be attributable to a fruit and vegetable intake below 500 and 800 g/day, respectively, if the observed associations are causal” and that ‘Fruit and vegetable intakes were associated with reduced risk of cardiovascular disease, cancer and all-cause mortality. These results support public health recommendations to increase fruit and vegetable intake for the prevention of cardiovascular disease, cancer, and premature mortality.” Skepticism is yet more called for since health and mortality are influenced to a great extent by genetics and geography, which were not controlled for.
The authors deserve credit, though, for the clause, “if the observed associations are causal”. What everyone should know about statistics is that correlations, associations, never prove causation. That law is almost universally ignored as the media disseminate press releases and other spin from researchers and their institutions, implying that associations are meaningful about what causes what.

It is easy enough to understand why considerable skepticism should be exercised with claims like those about mortality and diet or about dementia and pollution, simply because studies to test these claims properly would need to include much larger numbers of subjects. But an even greater reason to doubt such claims, as well as claims about newly approved drugs and treatments, is that the statistical analyses commonly used are inherently flawed, most particularly by a quite inadequate criterion for statistical significance.

Almost universally in social science and in medical science, statistical significance is defined as p≤0.05: the probability that the results are mere coincidence, owing just to random chance, is less than 5%, in other words less than 1 in 20.

Several things are wrong with that. Among the most serious are:

  1. That something is not a coincidence, not owing to random chance, does not tell us what it is owing to, what the cause is. It is not necessarily the experimenter’s hypothesis, yet that is the assumption made universally with this type of statistical analysis.
  2. 1 in 20 is a very weak criterion. It means that 1 in every 20 “statistically significant” conclusions is wrong. Do 20 studies, and on average one of them will be “statistically significant” even though it is wrong.
  3. That something is statistically significant does not mean that the effect is meaningful.
    For example, after I had a TIA (transient ischemic attack, minor stroke), the neurologist automatically prescribed the “blood thinner” Plavix, clopidogrel, as lessening the risk of further strokes. I am wary of all drugs since they all have “side” effects, so later I searched the literature and found that Plavix is statistically significantly better at decreasing risk than is aspirin, p = 0.043, better than p≤0.05. However, the relative efficacies found were just 5.83% compared to 5.32%; to my mind, not at all a significant difference, not enough to compensate for the greater risk of “side” effects from clopidogrel than from aspirin which has been in use for far longer by far more people without discovery of seriously dangerous “side” effects. (Chemicals don’t have two types of effect, main and side, those we want and those we don’t want. “Side” effects are just as real as the intended effects.)

Many statisticians have pointed out for many years what is wrong with the p-value approach to statistics and its use in social science and in medical science. More than two decades ago, an editorial in the British Medical Journal pointed to “The scandal of poor medical research” [i] with incompetent statistical analysis one of the prime culprits. Matthews [ii] has explained clearly point 1 above. Colquhoun [iii] explains that p ≤ 0.05 makes for wrong conclusions even more often than 1 in 20 times: “If you use p=0.05 to suggest that you have made a discovery, you will be wrong at least 30% of the time”. Gigerenzer [iv] has set out in clear detail the troubles with the commonly used p-value analysis.
Nevertheless, this misleading approach continues to be routine, standard, because it is so simple that many researchers who have no real understanding of statistics can use it. Among the consequences is that most published research findings are false [v] and that newly approved drugs have had to be withdrawn sooner and sooner after their initial approval [vi].
Slowly the situation improves as systemic inertia is penetrated by a few initiatives. A newly appointed editor of the journal Basic and Applied Social Psychology (BASP) announced that p-value analyses would no longer be required [vii], and soon after that they were actually banned [viii].

In the meantime, however, tangible damage is being done by continued use of the p-value approach in the testing and approval of prescription drugs, which adds to a variety of deceptive practices routinely employed by the pharmaceutical industry in clinical trials, see for example Ben Goldacre, Bad Pharma: How Drug Companies Mislead Doctors and Harm Patients (Faber & Faber, 2013); Peter C. Gøtzsche, Deadly Medicines and Organised Crime: How Big Pharma Has Corrupted Healthcare (Radcliffe, 2013); David Healy, Pharmageddon (University of California Press, 2012). Gøtzsche and Healy report that prescription drugs, even though “properly” used, are the 3rd or 4th leading cause of death in developed countries.

***************************************************************************

[i] D G Altman, BMJ, 308 [1994] 283

[ii] Matthews, R. A. J. 1998. “Facts versus Factions: The use and abuse of subjectivity in scientific research.” European Science and Environment Forum Working Paper; pp. 247-82 in J. Morris (ed.), Rethinking Risk and the Precautionary Principle, Oxford: Butterworth (2000).

[iii] David Colquhoun, “An investigation of the false discovery rate and the misinterpretation of p-values”, Royal Society Open Science, 1 (2014) 140216; http://dx.doi.org/10.1098/rsos.14021

[iv] Gerd Gigerenzer, “Mindless statistics”, Journal of Socio-Economics, 33 [2004] 587-606)

[v] (John P. A. Ioannidis, “Why most published research findings are false”, PLoS Medicine, 2 [#8, 2005] 696-701; e124)

[vi] Henry H. Bauer, Dogmatism in Science and Medicine: How Dominant Theories Monopolize Research and Stifle the Search for Truth, McFarland, 2012, Table 5 (p. 240) and text pp. 238-42

[vii] David Trafimow, Editorial, Basic and Applied Social Psychology, 36 (2014) 1-2

[viii] (David Trafimow & Michael Marks, Editorial, BASP, 37 [2015] 1-2; comments by Royal Statistical sociry[viii] and at https://www.reddit.com/r/statistics/comments/2wy414/social_psychology_journal_bans_null_hypothesis/)

Posted in media flaws, medical practices, peer review, prescription drugs, unwarranted dogmatism in science | Tagged: , , , , | Leave a Comment »

Speaking Truth to Big Pharma Power

Posted by Henry Bauer on 2017/03/18

Some time ago I recommended the newsletter of Mad in America, a diligent and reliable commentary on the flaws of modern psychiatric medicine.

A recent issue had links to a superb series of articles by David Healy, a psychiatrist who has spoken truth to Big Pharma and to the conventional (lack of) wisdom, at considerable personal cost. Healy also founded a website with information about dru side effects, RxRisk:
Tweeting While Psychiatry Burns
Tweeting while Medicine Burns (Psychopharmacology Part 2)
Burn Baby Burn (Psychopharmacology Part 3)

Also useful in this newsletter, link to a report of a meta-analysis confirming the Minimal Effectiveness and High Risk of SSRIs

Posted in conflicts of interest, medical practices, politics and science, prescription drugs, science is not truth, scientific culture, scientists are human | Tagged: , , | Leave a Comment »

Modern medicine: danger to public health and public purse?

Posted by Henry Bauer on 2017/02/23

Healthcare costs in the USA are now unmanageable, as illustrated by the common bankruptcies of people without good insurance and, just now, by the realization that the Republican promise to “repeal and replace Obamacare” is unworkable if many Americans are not to lose the insurance help they currently have.

One way to reduce costs that is not talked about, and that is unlikely to gain much traction until the crisis becomes catastrophic, would be to call a halt to medical  treatments that do harm rather than good. It comes as a surprise to learn, for example, that prescription drugs, used as prescribed, are the 3rd or 4th leading cause of death in developed nations (Gøtzsche, Peter C. Deadly Medicines and Organised Crime: How Big Pharma Has Corrupted Healthcare. Oxford & New York: Radcliffe, 2013
Healy, David. Pharmageddon. University of California Press, 2012).

A recent article at ProPublica and in  The Atlantic has much information  about unnecessary, often harmful practices that continue in routine medical practice:

When Evidence Says No, But Doctors Say Yes
Years after research contradicts common practices, patients continue to demand them and doctors continue to deliver. The result is an epidemic of unnecessary and unhelpful treatment.
David Epstein, ProPublica    February 22, 2017
This story was co-published with The Atlantic.”

The interests vested in present ways of doing things are many and powerful — clinics, hospitals, professional guilds, but chiefly the pharmaceutical industry. So it will not be easy to change the system, despite the fact that dozens of books and articles over the last few decades have described in documented detail what’s wrong with modern medicine.

Posted in medical practices, prescription drugs | Tagged: | 2 Comments »

Anti-psychotic drugs: initial benefit, long-term harm

Posted by Henry Bauer on 2016/08/03

Recently (Trust medical science at your peril (2): What is the evidence, especially in psychiatry? ) I recommended the newsletter of Mad in America  for disseminating reliable information about psychiatric matters. A recent issue of the Newsletter has links to a very thorough examination of the evidence that anti-psychotic drugs make things worse if used long-term: “The case against antipsychotics — A review of their long-term effects”, by Robert Whitaker (July 2016).

There is considerable support for the hypothesis that psychotic episodes are associated with heightened sensitivity to dopamine. Anti-psychotics ameliorate such episodes by blocking dopamine receptors. These drugs appear to be beneficial immediately, and for perhaps as long as a couple of years. However, once exposed to the drugs, withdrawal almost always has severe bad effects.

It appears that the brain tries to overcome the blocking of the dopamine receptors by increasing the number of these receptors. That takes appreciably long time, apparently many months if not years, so the consequences become significantly important only eventually. That explains why withdrawal brings even worse symptoms than the original ones were, and why long-term treatment is more harmful than beneficial. The drugs must be used forever, and their cumulating “side” effects are very debilitating.

Non-drug treatment of schizophrenia and other psychoses, sometimes teamed with short-term drug use, has much better long-term outcomes than does continual medication; better outcomes in terms of better all-around functioning and fewer relapses.

 

Posted in medical practices, prescription drugs, resistance to discovery, science is not truth | Tagged: | 4 Comments »

What to believe? Science is a red herring and a wild-goose chase

Posted by Henry Bauer on 2016/07/24

To be certain about things is reassuring. It allows feelings of safety, security.

For knowledge, for understanding the world, humankind seems to have turned at first to what could be inferred from the spirits of things — the spirits associated with or inherent in everything: in mountains, in trees, in bodies of water. The spirits could be understood, at least partly, because they were similar to people in having emotions and desires.

Eventually — quite recently, only a few thousand years ago — the plurality and hierarchies of spirits and gods yielded to monotheistic religions in most parts of the world. Even more recently, and only in the most powerfully developed countries, religion yielded to science.

That is to say, traditional religion yielded to scientism, the religion of science. Even the monotheistic gods have emotions and desires, but science doesn’t. So knowledge became entirely impersonal, at least in principle.

Nowadays, then, for real certainty we look to science. “Scientific” stands for unquestionably true. Science is the gatekeeper of truth. “Science” and “scientific” are mediators of being certain, being sure about something.

Consequently, a great deal of arguing to-and-fro has to do with whether something is scientific:
Does it emerge from use of the scientific method?
Is it reproducible?
Is it falsifiable?

And if a claim doesn’t satisfy those criteria or equivalent ones then it’s dismissed as not scientific, or as pseudo-science, or as just plain not to be believed.

That’s an indirect way of judging believability, and arguments about whether something is scientific can be and have been highly abstract, complicated, and sophisticated as technical philosophical discourse tends to be.

Instead, why not go directly at the issues of certainty and truth and just ask, what does it take to be justifiably and reliably certain about something?

In any case, although we use science as mediator of certain truth, we’ve also learned that contemporary scientific knowledge and understanding really isn’t always reliably true. Even when an explanation has been based on tangible evidence, and withstood challenges and tests — if it’s properly scientific, in other words — we’ve learned that it may be misleading. Scientific progress with periodic scientific revolutions has continually revealed flaws, deficiencies, errors, in what were for a time the most widely and fully accepted scientific theories.

If something has always happened in the past, can we be certain that it always will happen in the future? We’ve learned that we cannot be quite certain.

When an explanation has always worked in the past, can we be certain that it always will work in the future? We’ve learned that we cannot be quite certain.

When tangible things are sub-divided into their ultimate components, those turned out to be nothing like objects accessible to direct human observation. They do not fit our concepts of particles or energy, although many of their reactions can be calculated using sometimes particle equations and sometimes wave equations. They behave sometimes as though they were locatable, delimited in space-time, and at other times appear to be “non-local”, not so delimited.

In other words, we’ve learned that we cannot get certain and humanly comprehensible understanding of everything about the whole of the natural world. It’s surely time to accept that, that human beings will never attain complete certainty.

That could be liberating. It would make more feasible pragmatic, non-ideological communication and cooperative action — if only we could be rid of the ideologues: the true believers in a religion, including the true believers in scientism, the religion of science. Anyone who claims complete certainty has insufficient warrant for that claim. The world and its behaviors can be known only within degrees of probability. Instead of arguing about whether something is scientific or whether it is true, we ought to be discussing plausibility, likelihood, utility, risk.

Instead of dismissing as pseudo-science the claims that Loch Ness Monsters are real animals, we should be content to say, “Feel free to believe that if the evidence seems to you sufficiently convincing. For my part, I’ll wait until someone shows me an actual specimen or an indubitable bit of one”. And similarly with yetis and other cryptids, and with UFOs, and with all other anomalous or Fortean reports or claims.

Instead of arguing over being for or against vaccination, we should ask for the statistical data of harm possibly caused by each specific vaccine. For instance, since in many countries the chance of becoming infected by polio is less than the risk of contracting polio from the oral vaccine. perhaps official sources might be less dogmatic about enforcing use of that particular vaccine (“Polio vaccines now the #1 cause of polio paralysis”; “Oral polio vaccine-associated paralysis in a child despite previous immunization with inactivated virus”; “Bill Gates’ polio vaccine program caused 47,500 cases of paralysis death“).

And so on. For every drug and every treatment, we should demand that the Food and Drug Administration require data on NNT and NNH — NNT: the number of patients needed to be treated in order that 1 patient benefit, compared with NNH: the number of patients who must receive a drug in order to have 1 patient experience harm [How (not) to measure the efficacy of drugs].  That would go a long way to decreasing the number of people nowadays being killed by prescription drugs, which are the 3rd or 4th leading cause of death in First-World countries (Peter C. Gøtzsche, Deadly Medicines and Organised Crime: How Big Pharma Has Corrupted

Healthcare [Radcliffe, 2013]; David Healy, Pharmageddon [University of California Press, 2012]).

We need more data and less dogmatism.

 

 

Posted in medical practices, prescription drugs, science is not truth, unwarranted dogmatism in science | Tagged: , , , , , , | Leave a Comment »

Trust medical science at your peril (2): What is the evidence, especially in psychiatry?

Posted by Henry Bauer on 2016/07/15

All too often, the evidence turns out to be nothing more than statistical association: “Trust medical science at your peril: Correlations never prove causation”.

A particular example of confusing association with causation is the reliance on biomarkers:

“The Institute of Medicine Report, Evaluation of Biomarkers and Surrogate Endpoints in Chronic Disease (IOM 2010), finds that none of the commonly used biomarkers is a valid measure of the illness it supposedly tracks. As to subsequent treatment, Järvinen et al. have pointed out that ‘There are no valid data on the effectiveness . . . [of] statins, antihypertensives, and bisphosphanates’ (the last, e.g. Fosamax, are prescribed against osteoporosis) — British Medical Journal, 342 (2011) doi: 10.1136/bmj.d2175.
That last quote is surely an astonishing assertion, given that innumerable individuals are being fed statins and blood-pressure drugs and bisphosphanates not because they feel ill in any way but purely on the basis of levels of biomarkers (bone density in the case of bisphosphanates)” (Everyone is sick?)

Supporting evidence is sadly lacking for a wide range of accepted, standard medical practices. For at least a couple of decades, insiders and well-informed observers have described and documented the failings of modern medicine: “What’s wrong with present -day medicine”.

Bad as things are with the treatment of physical illnesses, they are much worse where psychiatry is involved. This blog post was stimulated by the informative article, “In Search of an Evidence-based Role for Psychiatry” by John Read, Olga Runciman, & Jacqui Dillon.

I had learned of it through the Newsletter of Mad in America, an excellent website dedicated to disseminating reliable information about psychiatric matters. One can sign up for the Newsletter at http://www.madinamerica.com/mia-newsletter-signup/.

 

Posted in conflicts of interest, consensus, medical practices, prescription drugs | Tagged: | Leave a Comment »

Trust medical science at your peril: Correlations never prove causation

Posted by Henry Bauer on 2016/06/28

It was a long-known empirical fact that poverty, vagrancy, criminality, and apparently deficient intelligence all correlated with heredity to a considerable extent; they all ran in families and clans. The scientific confirmation that characteristics of animals are passed on from generation to generation, and the Darwin-Wallace explanation of evolution by natural selection of the fittest, made it possible to understand those aspects of human society. It was an obvious, scientifically sound conclusion that human societies could be steadily improved by restricting reproduction of the less fit and expanding the fertility of the fittest. Hence the eugenics movement, promoted by the most progressive, liberal people who were also the best educated, with an apparently justified faith in the reliability of what was at the time the most up-to-date the scientific knowledge (Trust science at your peril: Beware of scientism and political correctness). Those circumstances led to forced sterilization of tens of thousands in America and reinforced Nazis in their doctrines and practices of mass killing of the unfit — Jews, gypsies, homosexuals (Edwin Black, War Against the Weak, 2003).

Only in hindsight did the flaws and errors of the earlier scientific consensus become clear. We now appreciate that environmental and developmental influences can modify heritable traits quite dramatically. “Ill-bred” can be the result of social, economic, environmental factors as much, perhaps even more than any pre-ordained verdict of genetics; and “well-bred” individuals can spring from what might seem the least promising hereditary stock. In other words, the observed correlation between undesired social characteristics and clans was misinterpreted through neglecting the variable of environmental effects.

One lesson to be drawn is that bad science, wrong science, what some even call pseudo-science, can remain the accepted scientific consensus for decades, even in quite modern times, say, the middle of the 20th century. It is unlikely that a mere half-a-century later our societies have become immune from assuming that a mainstream scientific consensus must be true to Nature. Nothing guards our times from treating unjustified, misguided scientific claims as good science.

Unwarranted claims coming from scientists continue to be accepted if they appear minimally plausible and if they are consistent with world-views and vested interests of financial, social, or political powers.

The most sweeping lesson that remains to be learned is that correlations must never be taken as demonstrating a cause-and-effect relationship: there might always be in play an unsuspected variable. One of the earliest axioms taught in Statistics 101 is that correlations never prove causation. The evident correlation between biological kinship and undesirable behavioral traits was not a cause-and-effect relationship.

Many or most people have never learned that basic truth that correlations are not causes. Many others “know” it as a generalization but fail to apply it in specific instances, when an evident correlation could plausibly reflect cause and consequence — just as a genetic basis for undesirable characteristics seemed quite plausible to educated and expert people not so long ago.

Indeed, a large swath of modern medical practices is based on mistaking mere correlations for evidence of causation (“Correlations: Plausible or implausible, NONE prove causation”). For example:

HPV and cervical cancer

The National Cancer Institute offers a great deal of information about this:

Human papillomaviruses (HPVs) are a group of more than 200 related viruses. . . Sexually transmitted HPV types fall into two categories:
— Low-risk HPVs, which do not cause cancer but can cause skin warts (technically known as condylomata acuminata) on or around the genitals, anus, mouth, or throat. For example, HPV types 6 and 11 cause 90 percent of all genital warts. HPV types 6 and 11 also cause recurrent respiratory papillomatosis, a less common disease in which benign tumors grow in the air passages leading from the nose and mouth into the lungs.
— High-risk HPVs, which can cause cancer. About a dozen high-risk HPV types have been identified. Two of these, HPV types 16 and 18, are responsible for most HPV-caused cancers. . . .
>> Cervical cancer: Virtually all cases of cervical cancer are caused by HPV, and just two HPV types, 16 and 18, are responsible for about 70 percent of all cases . . . .
>> Anal cancer: About 95 percent of anal cancers are caused by HPV. Most of these are caused by HPV type 16.
>> Oropharyngeal cancers (cancers of the middle part of the throat, including the soft palate, the base of the tongue, and the tonsils): About 70 percent of oropharyngeal cancers are caused by HPV. In the United States, more than half of cancers diagnosed in the oropharynx are linked to HPV type 16 (9).
>> Rarer cancers: HPV causes about 65 percent of vaginal cancers, 50 percent of vulvar cancers, and 35 percent of penile cancers (. . . .) Most of these are caused by HPV type 16.

The Centers for Disease Control & Prevention offer advice on avoiding HPV cancers:

— Bivalent, quadrivalent and 9-valent HPV vaccines each target HPV 16 and 18, types that cause about 66% of cervical cancers and the majority of other HPV-associated cancers in both women and men in the United States. 9-valent HPV vaccine also targets five additional cancer causing types (HPV 31, 33, 45, 52, 58) which account for about 15% of cervical cancers. Quadrivalent and 9-valent HPV vaccines also protect against HPV 6 and 11, types that cause anogenital warts.
— Quadrivalent and 9-valent HPV vaccines are licensed for use in females and males; bivalent HPV vaccine is licensed for use in females.
What percent of HPV-associated cancers in females and males are caused by the 5 additional types in the 9-valent HPV vaccine?
— About 14% of HPV-associated cancers in females (approximately 2800 cases annually) and 4% of HPV-associated cancers in males (approximately 550 cases annually) are caused by the 5 additional types in the 9-valent HPV vaccine.

What evidence is there for these extremely specific claims of causation?

None, actually. The cited facts are merely that the stated strains of HPV have been detected in those proportions of those cancers. Those correlations don’t begin to indicate causation.

It may be worth recalling that the Centers for Disease Control & Prevention in the early 1990s had officially stated, on the basis of the same sort of data (epidemiology, i.e. correlations), that cervical cancer was an AIDS disease, caused by HIV.

One may sympathize with medical researchers for the impossibility of conducting experiments that would be capable of proving cause-and-effect; ethical, legal, and moral restraints make it unfeasible to use human beings as experimental guinea pigs. There would also be practical barriers: To determine whether a given treatment, in this case a vaccine, actually prevents cancer, a clinical trial would be necessary that spanned over decades and enrolled large numbers of human guinea-pigs, some of whom (controls) would not get potentially-cancer-preventing vaccine.

However, the inability to obtain proof does not justify proclaiming as fact, as these official agencies do, causative relations that are no more than speculation based on statistical correlations.

[The vaccines] “Gardasil and Cervarix have not been shown to be of any significant health benefit. They have been demonstrated to cause serious injuries. It’s scandalous that they were ever approved, and it’s scandalous that they remain on the market.

And they are far from alone on those scores among new prescription medications introduced in the last couple of decades” (Deadly vaccines, 2013/04/17 http://wp.me/p2VG42-24).

Alzheimer’s Disease

Sleep disorders may raise risk of Alzheimer’s, new research shows
Sleep disturbances such as apnea may increase the risk of Alzheimer’s disease, while moderate exercise in middle age and mentally stimulating games, such as crossword puzzles, may prevent the onset of the dementia-causing disease, according to new research to be presented Monday

A daily high dose of Vitamin E may slow early Alzheimer’s disease

Again, these are correlations speculated to be possible causes.

Semantics no doubt plays a role. One could report that sleep disorders, and lack of vitamin E, seem to be associated with a risk of Alzheimer’s. Medical jargon puts it like this: “sleep disorders, and lack of vitamin E, are risk factors for Alzheimer’s”. Then the media and public conclude that “risk factor” means something that tends to cause the associated effect.

See also “60 MINUTES on aging — correlations or causes?

Biomarkers

It is not feasible to test treatments for chronic conditions by actual outcome, because one would have to wait a couple of decades to determine whether regimen A or drug B reduces morbidity and mortality apparently associated with high blood pressure, or high cholesterol, or high blood sugar, or low bone density, etc. All those are statistically correlated with increased morbidity and mortality. They are risk factors.

Present-day medical dogma makes them biomarkers for cardiovascular disease, diabetes, bone fracture, in other words indicators of whether the disease is present. But that is tantamount to making those quantities measures of actual risk, in other words regarding them as measures of what causes those ailments, in other words equating risk factors with causes.

Official reports, however, as well as the many studies on which those reports are based, find that biomarkers are not proper measures of risk after all. See:

“Everyone is sick?”

“‘Hypertension’: An illness that isn’t illness”

“Cholesterol is good for you”

 

Unfortunately, they were not joking

“Magical statistics: Hearing loss causes dementia”

 

The overall lesson:

“Don’t take a pill if you’re not ill”

The ignorant acceptance of correlations as capable of demonstrating causation is greatly reinforced in medical matters by the pharmaceutical industry, which sells drugs as palliatives and preventatives based on nothing more than correlations with biomarkers.

Posted in conflicts of interest, consensus, media flaws, medical practices, prescription drugs | Tagged: , , , , , , , | 4 Comments »

All vaccines are not the same; some are worse than useless

Posted by Henry Bauer on 2015/07/02

I am not among those who question the value of all vaccines on principle. I don’t doubt the value of vaccines in controlling smallpox, measles, polio. I do question the use of adjuvants and preservatives in vaccines, and I do think it makes sense to vaccinate babies against measles and the rest in single shots administered over a period of time instead of all at once in multiple vaccines.

But it gets difficult not to over-react as Big Pharma concentrates on generating vaccines that do more harm than any good that has ever been proven.

It seems that Big Pharma has been running out of new diseases to invent (see Moynihan & Cassels, Selling Sickness: How the World’s Biggest Pharmaceutical Companies Are Turning Us All Into Patients and other works listed in “What’s Wrong with Present-Day Medicine”) and has been turning increasingly to inventing vaccines supposed to guard against old or new infections.

The expected but not forthcoming “swine flu” epidemic led to rapid invention and marketing of a vaccine that turned out to have nasty “side” effects, for example, “How a swine flu shot led to narcolepsy”.

Gardasil and Cervarix, anti-HPV vaccines claimed to prevent cervical cancer, are a scandalous illustration; see for example “Merck Dr. Exposes Gardasil as Ineffective, Deadly, Very Profitable”  and related links. The only suggestion that HPV causes cervical cancer — or rather, that 4 out of four or five times that number of strains of HPV cause cervical cancer — comes from a correlation: those strains have often been found in women who have cervical cancer.

But correlations never, never, never prove causation, no matter that too many medical “experts” ignore this well established, long established fact.

I’ve become all too cynical about Big Pharma, lack of regulation, conflicts of interest, and the like. Yet I was taken aback to find that the National Institutes of Health profit from royalties from sales of Gardasil, and that there are exemptions to the Freedom of Information Act that enable them to hide that fact and the amounts involved.

Posted in conflicts of interest, fraud in medicine, funding research, legal considerations, medical practices, politics and science, prescription drugs | Tagged: , , | 7 Comments »

Who looks at evidence? Almost no one

Posted by Henry Bauer on 2015/06/28

I’ve been a crank for a long time about Loch Ness Monsters, frustrated because I can’t get people to look at Tim Dinsdale’s 1960 film which shows quite clearly a huge animal swimming in Loch Ness, submerging while still throwing up a massive wake.

For more than a decade, I’ve been a crank about HIV not causing AIDS, frustrated because I can’t get people to look at the clear evidence that HIV tests don’t track something infectious, and that the numbers in plain sight on the website of the Centers for Disease Control & Prevention, rates of sexual transmission at less than 1 per 1000 acts of unprotected intercourse, mean that HIV cannot cause an epidemic.

Now I’ve become a crank about human-caused climate change, frustrated because people won’t look at the clear evidence that carbon dioxide has been increasing steadily even as the global temperature was level or dropping form the 1940s into the 1970s, when the experts were predicting an Ice Age; and as the global temperature has not increased since the end of the 1990s.

Why don’t people look at evidence?

Because, I’ve finally realized, they don’t want to risk having to change their mind. There is no positive incentive and plenty of negative incentive. It’s beyond cognitive dissonance, which is to evade the significance of evidence after having come across it. It’s obviously even better not to have come across the evidence at all.

On human-caused climate change (HCCC), disbelief is expressed loudly and publicly by “conservatives” (in my view more accurately described as reactionaries) who have that opinion for the wrong reasons, namely the belief that economic free markets are the most important thing and regulating anything is bad.

“Liberals” or “progressives”, on the other hand (who are actually not liberal or progressive but simply knee-jerk politically correct) don’t look at the evidence because they don’t need to, it’s of no interest to them, they would take their stance that humans cause environmental damage no matter what. And they maintain perfect deniability, they are blameless, they were just accepting what the authorities, the experts, have been saying loudly and incessantly.

Most of my family and friends treat my “reactionary” stance on HCCC as a minor flaw, allowing me space because I tend to get caught up in Quixotic stuff all the time. They have no interest in looking at the evidence because they are completely comfortable with the notion of HCCC because it fits their anti-reactionary political views — which I happen to share. If it turns out that this HCCC is mistaken, there would be all sorts of undesirable consequences, in particular that reactionary views might appear to have been vindicated.

I was distressed when Stephen Colbert took HCCC as proven. I am not happy when all the MSNBC crowd does so, but they’ve become too extreme for me anyway and I rarely watch. But I was very unhappy when Jon Stewart took HCCC as proven. And Pope Francis may have been the last straw (in the wind, as far as ever changing public opinion). Though I did get a sort of sardonic enjoyment from the pundits who pointed out that the Pope knew what he was talking about because he had been a chemist. And I am getting continuing Schadenfreude over the contortions of the Republican presidential candidates as they are forced to comment on the Pope’s encyclical.

Evidence-seeking, I realize, is an obsession of perhaps the tiniest minority there is. On the dangers of modern medical practice, there are just a few dozen voices crying out publicly in the wilderness. On HIV/AIDS, there is our Rethinking AIDS  group of some dozens of people, with a few thousand more quietly agreeing. On HCCC, there are a few academic types like myself who got here because of the evidence, and who subsist uncomfortably in the association with people whose political and social views we do not share, to put it mildly.

I’m beginning to accept that none of the items in my bucket list will see the light of an enlightened day within my lifetime: Nessie discovery, rejection of HIV=AIDS, rejection of carbon-dioxide-is-hurting-us.

But I do remain curious about how the “authorities” will adjust when reality eventually catches up with them irrevocably.

[Corrected 8 August 2015 in paragraph 7]

Posted in consensus, denialism, fraud in medicine, fraud in science, global warming, media flaws, medical practices, politics and science, science is not truth, science policy, unwarranted dogmatism in science | Tagged: , , , , | 11 Comments »

How (not) to measure the efficacy of drugs

Posted by Henry Bauer on 2015/02/19

Innumerable books and articles have described the flaws of contemporary drug-based medicine, notably the way drugs are approved: the Food and Drug Administration requires only 2 successful trials of 6 months duration — even if there have been many unsuccessful trials as well. Accordingly, drugs have had to be withdrawn from the market because of their toxicity sooner and sooner after their initial approval (p. 238 ff. in Dogmatism in Science and Medicine, McFarland 2012). It is becoming quite common to see a drug being advertised by its manufacturers at the same time as a law firm is canvassing for patients harmed by the drug to join their class-action suit (today, for example, with Xarelto, approved in 2008 and for extended uses in 2011).

Not widely noted or understood is that the statistical criterion for efficacy of a drug is inappropriate. What concerns patients (and ought to concern doctors) is how big an effect a drug has; but the approval process only requires that it be better than placebo, or than a competing drug, at “statistical significance” of p≤ 0.05. The latter is already a very weak criterion, allowing the result to be wrong once in 20 trials. But even more inappropriate is that the effect size need not be large. If one uses a large enough number of guinea pigs, even a tiny difference can become “statistically significant”. For instance, clopidogrel (Plavix) is prescribed for prevention of stroke, and a study found it better at 75 mg/day, at statistical significance of p = 0.043, than aspirin at 325 mg/day. But it took nearly 20,000 trial subjects to reach this conclusion, because the reduction in risk of an adverse event was only from 5.83% (per year) to 5.32% *. One might judge this as trivial and not worth the extra cost and extra danger of side effects compared to aspirin, one of the safest drugs as demonstrated by decades of use.

Moreover, meaningful for patients is the change in absolute risk brought about by an intervention, not the relative reduction in risk compared to something else. The occurrence of an adverse (stroke) event is about 5% per year in older people; the absolute reduction brings it to perhaps 4.5%, about 1 in 22 instead of 1 in 20. Trivial, especially considering that such small differences, even from large trials, may actually be artefacts of some flaw or other in the trial protocol or practice.

The easiest measure of efficacy to understand, but almost never shared with patients or doctors, is NNT: the number of patients that needs to be treated in order to achieve the desired result in 1 patient. These numbers reveal an aspect of drug treatment that is not much emphasized: no drug is 100% effective in every patient.
Even less commonly shared is NNH: the number of patients who must receive a drug in order to have 1 patients harmed by that drug. This reveals an aspect of drug treatment that is not at all emphasized, indeed deliberately avoided: every drug has adverse effects to some degree.

A fine exposition of this appeared in the New York Times: “How to measure a medical treatment’s potential for harm”: to prevent 1 heart attack over a 2-year period, 2000 patients need to be treated (NNT = 2000 — the benefit is 1 in 1000); but aspirin can also cause bleeding, NNH = 3333. So the chance of benefit — very small to start with — is only about twice the chance of harm. In other cases — mammograms are mentioned, and antibiotics to treat ear infections in children, NNH is large compared to NNT; yet current medical practice goes against this evidence.

More examples are given by Peter Elias.

Statins show up very badly indeed when evaluated in this manner:

StatinsNNT

 

For other critiques of using statins, see “STATINS are VERY BAD for you, especially FOR YOUR MUSCLES”;  “Statins weaken muscles by design”;  “Statins are very bad also for your brain”;  “Statins: Scandalous new guidelines”.

——————————————————————
* Melody Ryan, Greta Combs, & Laroy P. Penix, “Preventing stroke in patients with Transient Ischemic Attacks”, American Family Physician, 60(1999) 2329-36

Posted in fraud in medicine, medical practices, prescription drugs | Tagged: , , , | 4 Comments »