Pharmageddon (13 page)

If all medicines are poisons, an outcome like this is not surprising. Simply recognizing that biology is complex highlights the risk of intervening and the need to test our assumptions and practices, no matter how benign the rationale for a particular approach might sound. An insistence on testing is exactly the spirit that gave rise to randomized controlled trials. They began as a means to control therapeutic enthusiasm, whether this enthusiasm came from the good intentions of physicians or from the greed of hucksters. What is there, then, about these trials that make companies so interested?

MIND THE GAP

In between treatments that are so obviously life-saving that trials are not needed and proposed remedies where trials save lives by demonstrating that the treatment doesn't work, there is the huge gap in which we have treatments that ease pain or restore function or promise some other benefit, even if a modest one. In the case of treatments that do not necessarily save lives but which equally cannot be dismissed as doing nothing, we are in much less certain waters than is usually realized. Controlled trials in these instances function primarily to bring to light both positive and negative associations between treatment and changes on a blood test or rating scale. It is in these waters that pharmaceutical companies have become adept at turning the evidence to their advantage.

Imagine an orthopedic department starting a trial on plaster casts for fractures of the left leg. As their placebo treatment they opted to have a cast put on the necks of the control group but in the active treatment group they randomly put casts on the right arm or leg, or left arm or leg of the patients, all of whom had broken left legs. The active treatment group in this case would do statistically significantly better than the placebo group but to advocate treating left leg fractures by indiscriminately putting a cast on any of four limbs on the basis that a randomized controlled trial had clearly shown this had worked would be nonsensical.
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Medicine in thrall to randomized controlled trials increasingly lets companies get away with just this, partly because an artful use of rating scales or blood tests conceals the fact that we don't know what we are doing. When we do know what is wrong the absurdity of simply practicing according to the figures becomes clear.

The plaster-cast example is not much more extreme than what in fact did happen in the case of the antidepressants.

When companies or their academics say today that a drug “works” what is commonly meant is that there is at least a minimal difference that is “statistically significant” between the effects of an active drug and a placebo on a blood test or rating scale. Evidence like this rather than evidence of lives saved or function restored, is all that the regulators need to let the drug on the market. Once approved for the market, the drug, be it for osteoporosis, cholesterol regulation, depression, or hypertension, is sold as though using it is the equivalent of being given penicillin or insulin. The problem is that increasingly, under the influence of company spin as to what the figures show, clinicians seem to prescribe drugs like the statins or antidepressants as though a failure to prescribe would leave them as open to a charge of clinical negligence as failing to prescribe insulin or penicillin would. The magic for companies lies in the fact that the numbers of patients recruited to the trials can be such that changes in rating scale scores or bone densities are statistically significant, whereas increased rates of death or other serious adverse events on treatment may not be.

When it comes to treating people who are supposedly depressed, anti- depressant credentials in the form of comparable changes on depression rating scales have been generated for most of the benzodiazepines, for a number of antihistamines, for almost all the stimulants, as well as for the antipsychotics and anticonvulsants,
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and they could be generated for nicotine or indeed for snake oil, whose omega-3 oils appear to have some psychotropic properties. A key difference between these diverse drugs and the selective serotonin reuptake inhibitors (the SSRIs) such as Paxil, Prozac, and Zoloft, was that the SSRIs were newly patented for treating depression, while drugs like nicotine or the antihistamines were unpatentable for this purpose. There was no incentive for companies to bring these latter drugs to the market but no reason to believe these drugs would be any less helpful than Prozac for depression. In the case of Prozac and Paxil, there is evidence of a weak association between treatment and a change on a rating scale but the question is what lies behind that change. The fact that so many quite different drugs can also be linked to a comparable benefit shows we know next to nothing about what is going on.

This is where the role of a mythic image of what a drug is supposed to do (a concept) can be of great importance to a marketing department. No one claims nicotine or benzodiazepines correct a lowering of serotonin in depression, whereas the SSRIs supposedly do. The idea that there is an imbalance of serotonin in depression is completely mythical. It arose in the marketing department of SmithKline Beecham, the maker of Paxil.
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The key thing about this myth is that it provides an image that functions like the imagery of bacterial colonies in a Petri dish shrinking back from an antibiotic, or images of cholesterol levels declining following treatment with statins, or bones becoming denser with biphosphonate treatment for osteoporosis. These images help create the impression that drugs “work,” when in fact the data from trials show these treatments have relatively minimal effects. These images create a spin that no data can overcome. Myths always have the last word.

How minimal are the treatment effects? In 2006 the FDA asked companies making antidepressants to submit all placebo-controlled trials to the agency. Just as some people recover from infections without treatment, based on 100,000 patients who had been entered into these anti- depressant trials, the data showed that four out of ten people improve within a few weeks whether treated with a drug or not.
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This may in part be due to the natural history of depressions in which 40 percent recover within a few months whether treated or not. Advice from a clinician on diet, lifestyle, alcohol intake, and problem solving on work and relationship issues may make a difference. Perception by patients that they are being seen and cared for by a medical expert may also make a difference, and this effect may be enhanced by being given a substance they think will restore some chemical balance to normal—even if that imbalance is mythical and the substance a placebo. On the active drugs, five out of ten apparently responded. But what comparing an active drug to a placebo shows us is that of these five, four (80 percent) of apparent responders to an antidepressant would have improved had they received the placebo. In other words, only one in every ten patients responds specifically to the antidepressant, whereas four in every ten treated with a placebo show a response.

If clinicians were really following the evidence, they should say that it's wonderful to have some evidence that antidepressants have benefits, but they would hold back on prescribing them indiscriminately and give a number of their patients a chance to recover without treatment. There is good reason to believe that many of those who recover without drug treatment are less likely to relapse in the longer run, which provides even more reason to wait judiciously in at least some cases.
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Given that the benefits obtained in the one out of ten are bought at a cost— overall more die on treatment than on placebo, more become dependent on treatment than on placebo, more on treatment have children born with birth defects than do those on placebo, and have many other side effects—the antidepressants arguably provide the perfect set of data to support Pinel's dictum that it is important to know when not to use a treatment.

On grounds of self-interest, there are good reasons for doctors to wait in many nonacute cases. Until recently the magic was in the therapist, who might also give pills, which were an extension of his or her impact on us. Now the magic has passed into the capsule and the physician often seems little more than a conduit for medication. Therapists have forgotten how influential they might be in promoting healthier lifestyles for conditions from raised cholesterol to the inevitable but relatively inconsequential thinning of bones that happens after the menopause. With the focus that both doctor and patient now have on taking a pill, seldom do either heed the context in which a person has become distressed or unhealthy. Neither doctor nor patient appears to see how small a contribution this chemical manipulation is likely to make or to see the potential for a chemical manipulation to make things worse. In practice, doctors end up so often doing what suits drug companies- they persuade patients to go on treatments. Why? In no small part because they have become convinced that these treatments have been shown in randomized controlled trials to work.

A consideration of these nondrug aspects of medical care doesn't just apply to drugs like the antidepressants. An antibiotic like penicillin might make a life-saving difference, but it's important to note that this may not be the only route to saving a life. Once the infection begins, an antibiotic may be by far the best way to help, and we would sue a doctor who let a patient die without treatment. But in the case of puerperal infections, long before the advent of penicillin, it had become clear that women were only likely to contract these disorders if they gave birth in hospitals where the infection could be transmitted readily from one woman to the next. Strict antiseptic procedures in hospitals could help, but giving birth outside the hospital made an infection much less likely.

At the moment doctors appear to be under increasing pressure from insurance managers, hospital bureaucrats, and others to hand out drugs in response to medical problems. If patients aren't on a treatment, they aren't in treatment. No one, not even an insurance manager, would want to be linked to unnecessary deaths. In using drugs that have been shown to “work” in a statistically significant fashion, all concerned think they are avoiding this possibility. But while penicillin can clearly be shown to save lives, the same clarity can't be found with antidepressants, statins given to people with no prior cardiovascular events, asthma drugs, or treatments for osteoporosis and many other conditions. In all these cases, a shrewd selection of statistically significant changes on rating scales or blood tests as evidence that the treatment “works” has been used by pharmaceutical companies to mesmerize all the key players.

“Working” in the case of all the best sellers in medicine, it bears repeating, means the drug produces changes on some measurement of interest to a drug company, rather than indicating the drug saves a life or returns someone to employment, or is better than an older drug in the field, or even makes a person simply feel better. When in the course of these trials patients are allowed to rate whether their quality of life has been improved, in results reminiscent of Sanjeebit Jachuk's study of propranolol, antidepressants, for instance, don't show any benefit over placebo. Such quality-of-life data from antidepressant trials are little known, however, because they remain almost universally unpublished.
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The bottom line is that while placebo-controlled trials have created appearances that the drugs work, with a few changes to the choice or rating scales or blood tests in these studies or taking into account the withdrawal effects many of these drugs have, it would be possible to show just the opposite for most of medicine's blockbusters.

There is a fundamental psychological issue here on which companies play, an issue illuminated by a series of experiments Daniel Kahnemann and Amos Tversky conducted in the 1970s on what happens when we are asked to make judgments under conditions of uncertainty.
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Kahnemann and Tversky, who won a Nobel Prize for their work, gave descriptions of a shy, retiring, and bookish personality to their test subjects and asked them to judge whether the person was a nurse or a librarian, having told them the personality profile had been drawn from a group that contains eight nurses and two librarians. Their subjects confidently said the person described was a librarian, when, given the probabilities, they should have said nurse. In the same way, statistics like those mentioned from the antidepressant trials (in which five out of ten seemed to improve from the drug, but closer inspection revealed that improvements in four out of those five could as well be due to placebo effects) should lead us, given the overwhelming odds, to attribute a positive response in a patient to a placebo effect. But like the subjects who chose librarian, we're more likely to jump to the conclusion that the antidepressant must have been the cause.

As drug marketers know, we are all more confident with stereotypes than with rational analysis of the probabilities of a situation. When we see patients on a pill recover, probably because of powerful examples like those of penicillin and insulin, we assume the recovery has come about because of the pill. This bias may be reinforced by hearing “experts” claim that antidepressants or statins work or by seeing these claims in what are considered authoritative publications. A mythic image of increasing bone density or normalizing serotonin levels or lowering cholesterol levels helps increase our certainty.

Neither clinicians nor patients are well equipped to make judgments based on data. Our psychology biases us against seeing what the data actually show, and this bias is aggravated by the selective publication of company trials that indicate a “positive” response to the drug and, ironically, by an apparatus put in place to ensure doctors adhere to the “evidence.” These factors have increasingly led to an almost automatic prescription of the latest drugs whether they are statins, hypoglycemics, biphosphonates, or psychotropic drugs.

COMPANY TRIALS

The job of medicine is to save lives, restore function, or improve on treatments already available. The aim of a drug company is to get their drugs on the market and generate profits by so doing. To see if a new treatment saves more lives or performs better than an older treatment, the obvious step is to compare the two. To get on the market, you could demonstrate superiority to an older treatment, but to satisfy the FDA or regulators in Europe or Japan you only have to beat the placebo. And if you recruit ever larger numbers of patients to trials, ever less clinically significant differences from placebo can become statistically significant. Perversely this will lead to the newer and weaker drug selling even better than the older one.