The Sober Truth (24 page)

The error of starting out with the belief that what you are looking for is likely to be meaningful was first formally recognized by Thomas Bayes, an eighteenth-century English minister and mathematician. He wrote that in experimental science, it is necessary to estimate the chance of each result
prior
to running an experiment. A good example of why this is important was given by the statistician Nate Silver (famous for accurately predicting virtually every state and national election result in the United States in both 2008 and 2012).
¹⁰
Silver points out how, for many years, the winner of the Super Bowl was widely said to predict the rise or fall of the stock market for the rest of that year. This was because starting with the first Super Bowl in 1967 and for the next thirty years until 1997, the stock market gained an average of 14 percent for the rest of the year when a team from the original NFL won the game, but fell almost 10 percent when a team from the original AFL won. Statistically, that correlation “showed” a definite connection between the two events. Indeed, there was just a one in five million possibility that this connection was due to chance alone! Without a foundation in Bayesian thinking, one would believe this to be incontrovertible proof that some as-yet-unidentified factor really did tie these two outcomes together. Of course they were mistaken, as the next fourteen years showed exactly the opposite result.

Bayes was intrigued by our tendency to seize upon absurd statistical conclusions like this and realized that relying on numbers alone was simply too shortsighted to make sense of statistics, or the world. Numbers contain precious little information about whether a correlation actually reflects a plausible reality or might instead be a statistical blip, hiccup, clump, or random anomaly.

In the case of the Super Bowl, for instance, those who breathlessly repeated and studied the coincidence as if it were significant forgot to ask an important question in plain language first: How could the winner of the Super Bowl have anything to do with the stock market? Bayes said that if you don’t take into account the likelihood of something being true
before
you interpret the results, then you are stepping into never-never land; failing to decide in advance whether the outcome is realistic robs us of any chance to describe reality. For the Super Bowl correlation, a moment’s thought would have given the likelihood of it being meaningful a very low probability. Applying Bayes’ theorem (a simple formula that takes into account the likelihood of an outcome’s being meaningful) would have yielded a result showing a very low chance that this measured statistic had any validity for the real world. As Ioannidis put it: “The probability that a research finding is indeed true depends on the prior probability of it being true (before doing the study), the statistical power of the study, and the level of statistical significance.”
¹¹

All the studies we have seen purporting to show the effectiveness of AA, for instance, begin with the assumption that the AA method is eminently reasonable. As proof, they offer references to each other. In investigating these papers, I found zero references to psychological views of addiction, which might have led the authors to decrease their estimate of the likelihood that their results were describing anything of value. In an insular field that has preemptively decided what it believes, meaningless findings are reinforced and consonant results are amplified without the counterbalance of skepticism. Ioannidis said it best:

The root of this error goes beyond mutually supporting belief systems. The addiction field has been dominated by two colossal institutions, neither of which is trained or interested in looking beneath the surface of any behavior to its underlying causes. One of these forces is AA. The other is the titanic shift in psychiatry away from the exploration of human psychology toward more reductive and behavioral models, including the very popular notion that addiction is a disease. Both are riddled with biases that preclude their investigation of more plausible mechanisms behind addiction.

The end goal of those who study human behavior for genetic markers and neurotransmitters is a seductive fallacy: the notion that someday, with perfect knowledge of our brain chemistry, we might somehow “unlock” the essence of human experience. It is a fallacy because it fails to recognize what more than thirty years of chaos and complex systems theory have already taught us: When networked pieces of
anything
come together, be they ants in a colony or neurons in a brain, the network exhibits
emergent
behaviors that are far more strange and complex than anyone could predict from looking at their constituent parts. Indeed, one of the tantalizing findings of this research is that often these behaviors have
nothing to do
with those constituent parts; they are, in a sense,
platform agnostic
. One of my favorite quotes by the Nobel laureate Philip Anderson encapsulates the point wonderfully: “Psychology is not applied biology, nor is biology applied chemistry.”
¹³

How do I know that my own bias toward a psychological perspective isn’t pushing me toward the same flawed and unfounded worldview? First, there is the commonsense fact that addiction looks just like known psychologically caused compulsions and can respond to purely psychological treatment; from a Bayesian standpoint, the idea that addictions and compulsions are intimately related is a sensible hypothesis. Those in favor of a biochemical model must contend with the fact that behaviors that truly are biochemical in origin, such as schizophrenia and mania, are fundamentally different from human addiction—they can arise and persist without psychological precipitants and can be treated with medication. Although these biochemical diseases create enormous distress, they do not have a specific emotional meaning or purpose; when appropriately treated with medication, people with these diagnoses can return to their usual state.

There are other objective factors supporting a psychological view of addiction. As we know from a large academic literature (see
The Heart of Addiction
for many references), as well as from common experience, addiction in humans follows psychological precipitants, which are idiosyncratic to each individual and predictable.
¹⁴
Addictive behavior can shift to compulsive symptoms that are universally understood to be psychological in nature, such as compulsively cleaning the house. And addiction can be successfully understood and treated by understanding how it works psychologically in each person through a talking treatment (psychotherapy). If we had never started out with the misconception that addictions are somehow different from other compulsive symptoms, we would not have made the error of separating them from the rest of the human condition to begin with.

These days, virtually every addiction journal assigns far more value to statistical studies than to clinical findings. The primary claim is that words are not rigorous; numbers are. Yet this perspective fails to account for the complexity of human beings, who are, let’s face it, not just more complex than rats, but more complex than any number could possibly assimilate. (If someone undergoes therapy and is now more comfortable in intimate situations, what number should we assign to that?)

Serious psychology journals usually manage this problem by reporting case studies rather than numbers. While individual cases have the limitation that they may not be generalizable to everyone, the accumulated wisdom from many case reports allows increased understanding of the way human beings’ minds work. If you wanted to learn about how radios work, you could take a thousand of them and subject them to an experiment, say, by dropping them off a building, then study the statistical likelihood of their having transistors. Or, you could start with one radio and carefully take it apart. True, there might be other radios that work differently, but after examining this one, you would know in broad strokes how radios work.

Case reports have tremendous value. They are, quite simply, the only way to describe treatment. They supply a level of detail, nuance, and narrative that doesn’t conform to statistical terms but contains more information. Therapy often yields common external and observable consequences of internal changes, but these may be impossible to measure except in the subjective experience of the patient. In the case of increased ability to tolerate intimacy, for instance, if a patient who has avoided closeness his entire life is now able to look someone in the eye and spend time talking instead of quickly hurrying away, that may be evidence of a life-changing alteration of his internal state that is deeply meaningful to the patient—yet ultimately immeasurable. Should we therefore discount it? Someone once said, “Not everything that is important can be measured, and not everything that can be measured is important.” This is nowhere more applicable than in the study of human emotions, behaviors, and experience. We don’t have a system of numbers for such things. But they couldn’t be more relevant to the question of addiction.

The memorable phrase “Lies, damn lies, and statistics,” commonly attributed to Mark Twain, was probably invented out of a combination of humor and pique. But statistics are neither good nor bad. In this book, I have cited statistics when there is no reason to doubt their legitimacy and criticized them when they are applied with bias or other methodological flaws. Perhaps most important, there are places where statistics have no role.

So how could we arrive at a more encompassing and broadly applicable consensus about what “works” in addiction treatment? The gold standard in science is the randomized controlled study. (No psychological study can be double-blind, which is the third common standard, as the psychologists administering the therapy will know which type of treatment they are offering.)

Let’s imagine what that study might look like. A large population (over multiple treatment centers around the country) would be randomly assigned into groups that would receive the standard of care in four different approaches, or modalities: cognitive behavioral therapy (CBT), psychodynamic therapy based on the modern understanding described in this book, a 12-step outpatient approach, and a control group given no treatment at all. All groups would be matched for relevant factors such as age, sex, race, income, and educational levels. Follow-up surveys and interviews would be conducted every month through the six-month mark, and then at one year, two years, three years, five years, ten years, and twenty years.

Shockingly, nobody has ever conducted such a study. Besides a dismaying lack of interest, the other reason is almost certainly money. Major public studies such as these can run well into the millions of dollars. And the organizations with the deepest pockets in this area have the strongest reasons to leave the current paradigm alone. It must ultimately fall to public science or to a wealthy university to get this kind of research off the ground. A fraction of what Americans spend on rehab would cover the entire study, and then some.

But the researchers would face some profound limitations as well. Psychodynamic work requires long-term follow-up, as well as assessment of outcomes beyond the symptom. One reason psychodynamic work requires long follow-ups is that major life-affecting improvement may occur during the treatment but before the addictive behavior ends. Therefore if the behavior alone is measured, then psychodynamic therapy may appear to be slower (hence less “effective”) when what is actually happening is that the causes of the behavior are being worked out before the behavior stops (though the addiction may end before the therapy is very far along, as I described in
Breaking Addiction
).

The relative capacity of therapists would also have to be determined, which is much harder to do than establishing baseline competence to administer questionnaires or perform therapy out of a workbook (commonplace for CBT). However, no universal standard of effectiveness for psychodynamic work has ever been established. In order to adequately test the theory of addiction I’ve described in my work, it would be necessary to train already-sophisticated psychodynamic clinicians in this new perspective. The good news is that this would actually not be difficult, since the model is entirely based on already established and accepted psychodynamic understandings.

The costs and logistics of doing a proper study would certainly be great, but could be completed with governmental support. Unfortunately, the government’s own agency (the National Institute on Drug Addiction) is deeply invested in its own neurobiological (“brain disease”) idea. And pharmaceutical companies, a ready source for research on drugs, would have nothing to gain by funding a study of psychodynamic treatment.

For the time being, until a critical mass is reached on pursuing the question of addiction treatment from a fuller perspective, the very best contribution individuals can make is to seek out therapists with good general psychological training (and without 12-step bias), and to apply pressure where it is needed to mount a public campaign in support of enlightenment in addiction research.

My hope is that the website for this book will become a rallying point for readers to coalesce around the disillusionment so many Americans feel with the current system—and provide a tipping point that leads us toward a better approach to this solvable problem.