Marie Curie, Geiger counters and mass hysteria: more in common than meets the eye

What do Marie Curie, a Geiger counter and mass hysteria have in common? Well, to answer this question we need to go Sir Arthur Eddington, who was a British astrophysicist and philosopher of science at the turn of the 20th century. He came up with what is frequently referred to as the Eddington parable, which has nothing to do with the stars specifically and everything to do with how we make scientific progress. Here it is for your reading enjoyment, as told in this editorial (available by subscriptionby Diamond and Kaul, two highly respected clinician-researchers:

Let us suppose that an ichthyologist is exploring the life of the ocean. He casts a net into the water and brings up a fishy assortment. Surveying his catch, he [concludes that no] sea-creature is less than two inches long. An onlooker may object that the generalization is wrong. "There are plenty of sea-creatures under two inches long, only your net is not adapted to catch them." The ichthyologist dismisses this objection contemptuously: "Anything uncatchable by my net is ipso facto outside the scope of ichthyological knowledge, and is not part of the kingdom of fishes which has been defined as the theme of ichthyological knowledge. In short, what my net can't catch isn't fish”.

Suppose that a more tactful onlooker makes a rather different suggestion: "I realize that you are right in refusing our friend's hypothesis of uncatchable fish, which cannot be verified by any tests you and I would consider valid. By keeping to your own method of study, you have reached a generalization of the highest importance—to fishmongers, who would not be interested in generalizations about uncatchable fish. Since these generalizations are so important, I would like to help you. You arrived at your generalization in the traditional way by examining the fish. May I point out that you could have arrived more easily at the same generalization by examining the net and the method of using it?"

So,you see my point? Tools determine knowledge. Period.

  

Read More

Tipping a sacred cow: QI under the microscope

So much media and journal space has been devoted to financial conflicts of interest, particularly within and related to pharma and device manufacturers, that to write any more about it may be redundant. On this site we have also intermittently addressed COI from other perspectives, such as financial interest of the members of the American College of Radiology in maintaining mammography screening status quo, thinly veiled in its own version of the pernicious "death panel" language. We have also spoken a bit about the non-financial COI. And even though we are so very much aware of COI's potential to lurk around every corner, there are still some surprises.

Take the sacred cow of "quality improvement" in healthcare. Even the name, much like the "pro life" moniker, suggests that it is untouchable in its purity and nobility of purpose. So necessary is it because of the epic magnitude of morbidity and mortality attributed to healthcare itself, that the billions of dollars spent on it seem unquestionably justified. Indeed, much like our public education system, the QI movement garners higher and higher allocations simply due to the sheer face validity of the assumption that more of it is better. And the most fascinating aspect is that, in our current zeal for sensible economic allocation through evidence, QI, much like education, appears immune to scrutiny. This is the very definition of politics driving policy.

I return to the case of ventilator-associated pneumonia, or VAP, as the poster child for this movement. I have already alluded to the fact that definitionally VAP is a slippery slope: its diagnosis varies based not only on the tools used to diagnose it, but also depending on who is doing the diagnosing. Yes, indeed, what one clinician calls VAP another may call absence of VAP. I have also dissected the weak evidence behind some of the strongest purportedly evidence-based recommendations aimed at VAP prevention. But what if VAP itself is the wrong endpoint? What if we are spending untold dollars and other resources on a futile pursuit?

Do you feel yourself bristling yet? If you said "yes", it is a normal response I get from my colleagues and people who read my scholarly papers. Because how can anyone be against QI? Well, I am not against QI. I am simply against sanctifying QI as a sacred cow and thus shielding it from a sensible and rational evaluation.

So, if you are over the initial shock, allow me to explain myself. I am sure you have heard of surrogate endpoints. Here is a definition from Wikipedia:

In clinical trials, a surrogate endpoint (or marker) is a measure of effect of a certain treatment that may correlate with a real clinical endpoint but doesn't necessarily have a guaranteed relationship. The National Institutes of Health (USA) defines surrogate endpoint as "a biomarker intended to substitute for a clinical endpoint".^[1][2]

Surrogate markers are used when the primary endpoint is undesired (e.g., death), or when the number of events is very small, thus making it impractical to conduct a clinical trial to gather a statistically significant number of endpoints. The FDA and other regulatory agencies will often accept evidence from clinical trials that show a direct clinical benefit to surrogate markers. ^[3]

This begs the question of what constitutes a "real" clinical endpoint. Well, in my simplemindedness I think of them as endpoints that matter to the patient or in the long run. So, death, disability, quality of life, functionality, these are the real endpoints. Something that alters one's life or threatens it is a real endpoint. Thus, blood pressure and cholesterol are surrogate endpoints, since they usually, but not always, correlate with the risk of a myocardial infarction or death. But what if such a correlation did not exist? Furthermore, what if a cholesterol level was measured with, say, tea leaves, and therefore was subject to a tremendous variation in detection? Would we then spend hundreds of billions of dollars on trying to alter this factor or would we calmly and rationally walk away and look for something that truly impacts the real outcome of a heart attack or death? I think I am making my point fairly clearly.


Let me explain why I think that VAP is but a surrogate outcome, and, given its diagnostic challenges, not a sensible one in the least. VAP by definition occurs in patients on mechanical ventilation (breathing machine), whose quality of life is fairly badly damaged in the short term. The literature would suggest that not all VAP impacts mortality adversely, but some forms of VAP indeed do, particularly VAP that develops late in the course of illness. So in this VAP does correlate with a real endpoint. Also, there is very little doubt that getting VAP prolongs one's dependence on mechanical ventilation, and increases the duration of the stay in the ICU and hospital overall. So, this can be considered not a very good, albeit real, outcome. An additional point to remember is that VAP engenders the use of additional, usually broad spectrum, antibiotics, putting both the individual and the society at risk for such unwanted consequences as the emergence of highly resistant microorganisms.


So, even though VAP is a surrogate endpoint, it certainly seems to fit the bill for something we would want to prevent. But here is the monkey wrench in this argument: what seem to be great surrogate endpoints do not always end up correlating with clinical reality. The association of VAP with morbidity and mortality has been detected in mostly retrospective observational studies. Trials of VAP prevention rarely, if ever, report any endpoint other than VAP. And, given how elusive VAP diagnosis is, there is plenty of room for gamesmanship so pervasive in the real world to make any data fit our preconceived hypotheses and political needs. 


So, what is my point? My point is that if QI wants to be a science, it needs to be subject to the same rules that all other science is guided by. Since we do not even know how much money we are spending on the ubiquitous QI efforts (likely hundreds of billions), and since we are not sure what they are accomplishing (see my many prior posts on the lack of validity of current claims in VAP prevention), we need to pause and ask ourselves whether the cheering alone justifies such an investment. I hate to say it, but can we really trust those with most to lose, financially and politically, if in reality QI does little more than lather the masses, to be the oracles of truth about the results of these efforts? The cognitive biases alone should disqualify them from being the arbiters of their own success. So, if we do not want to continue to indulge the principle of diminishing returns in QI, we need to take a sober look at what we have invested and what this investment has accomplished. Then and only then can we claim to practice evidence- rather than politics-or dogma-based policy.

Read More

The beautiful uncertainty of science

I am so tired of this all-or-nothing discussion about science! On the one hand there is a chorus singing praises to science and calling people who are skeptical of certain ideas unscientific idiots. On the other, with equal penchant for eminence-based thinking, are the masses convinced of conspiracies and nefarious motives of science and its perpetrators. And neither will stop and listen to the other side's objections, and neither will stop the name-calling. So, is it any wonder we are not getting any closer to the common ground? And if you are not a believer in the common ground, let me say that we are only getting farther away from the truth, if such a thing exists, by retreating further into our cognitive corners. These corners are comfortable places, with our comrades-in-arms sharing our, shall we say, passionate opinions. Yet this is not the way to get to a better understanding.

Because I spend so much time contemplating our larger understanding of science, the title "Are We Hard-Wired to Doubt Science" proved to be a really inflammatory way to suck me into thinking about everything I am interested in integrating: scientific method, science literacy and communication and brain science. The author, on the heels of doing a story on the opposition to smart meters in California, was led to try to understand why we are so quick to reject science:

But some very intelligent people I interviewed had little use for the existing (if sparse) science. How, in a rational society, does one understand those who reject science, a common touchstone of what is real and verifiable?

The absence of scientific evidence doesn’t dissuade those who believe childhood vaccines are linked to autism, or those who believe their headaches, dizziness and other symptoms are caused by cellphones and smart meters. And the presence of large amounts of scientific evidence doesn’t convince those who reject the idea that human activities are disrupting the climate.

She goes on to think about the different ways of perceiving risk, and how our brains play tricks on us by perpetuating our many cognitive biases. In essence, new data are unable to sway our opinion because of rescue bias, or our drive to preserve what we think we know to be true and to reject what our intuition tells us is false. If we follow this argument to its logical conclusion, it means that we just need to throw our hands up in the air and accept the status quo, whatever it is.

I happen to think that the author missed an opportunity to educate her readers about why we need to come to a better understanding and how to get there. The public (and even some of my fellow scientists) needs to understand what science is and, even more importantly, what it is not.

First, science is not dogma. Karl Popper had a very simple litmus test for scientific thinking: He asked how you would go about disproving a particular idea. If you think that the idea is above being disproved, then you are engaging in dogma and not science. The essence of scientific method is developing an hypothesis from either a systematically observed pattern or from a theoretical model. The hypothesis is necessarily formulated as the null, making the assumption of no association the departure point for proving the contrary. So, to "prove" that the association is present you need to rule out any other potential explanation for what may appear to be an association. For example, if thunder were always followed by rain, it might be easy to engage in the "post hoc ergo propter hoc" fallacy and conclude that thunder caused rain. But before this could become a scientific theory, you would have to show that there was no other explanation that would disprove this association.

So, the second point is that science is driven by postulating and then disproving the null hypotheses. By definition, an hypothesis can only be disproved if we 1). the association exists, and 2). the constellation of phenomena is not explained by something else. And here is the third and critical point, the point that produces equal parts frustration and inspiration to learn more: That "something else" as the explanation of a certain association is by definition informed only by what we know today. It is this very quality of knowledge production, the constancy of the pursuit, that lends the only certain property to science, the property of uncertainty. And our brains have a hard time holding and living with this uncertainty.

The tension between uncertainty and the need to make public policy has taken on a political life of its own. What started out as a modest storm of subversion of science by politics in the tobacco debate, has now escalated into a cyclone of everyday leveraging of the scientific uncertainties for political and economic gains. After all, how can we balance the accounting between the theoretical models predicting climate doom in the future and the robust current-day economic gains produced by the very pollution that feeds these models? How can we even conceive that our food production system, yielding more abundant and cheaper food than ever before, is driving the epidemic of obesity and the catastrophe of antimicrobial resistance? And because we are talking about science, and because, as that populist philosopher Yogi Berra famously quipped, "Predictions are hard, especially about the future," the uncertainty of our estimates overshadows the probability of their correctness. Yet by the time the future becomes present, we will be faced with potentially insurmountable challenges of a new world.

I have heard some scientists express reluctance about "coming clean" to the public about just how uncertain our knowledge is. Nonsense! What we need under the circumstances is greater transparency, public literacy and engagement. Science is not something that happens in the bastions of higher education or behind the thick walls of corporations. Science is all around and within us. And if you believe in God, you have to believe that God is a scientist, a tinkerer, always looking for a more elegant solution. The language of science that may seem daunting and obfuscatory. Yet do not be afraid -- patterns of a language are easy to decipher with some willingness and a dictionary. Our brains are attuned to the most beautiful explanations of the universe. Science is what provides them.

Self-determination is predicated upon knowledge and understanding. Abdicating our ability to understand the scientific method leaves us subject to political demagoguery. Don't be a puppet. We are all born scientists. Embrace your curiosity, tune out the noise of those at the margins who are not willing to engage in a sensible dialogue, leave them to their schoolyard brawling. And likewise, leave the politicians, corporate interests, and, alas, many a journalist, and start learning the basics of scientific philosophy and thought. Allow the uncertainty of knowledge excite and delight you. You will not be disappointed.                   
              

Read More

Reviewing medical literature, part 1: The study question

Let's start at the beginning. Why do we do research and write papers? No, not just to get famous, tenured or funded. The fundamental task of science is to answer questions. The big questions of all time get broken down into infinitesimally small chunks that can be answered with experimental or observational scientific methods. These answers integrated together provide the model for life as we understand it.

Clearly, the question is the most important part of the equation, and this is why in my semester-long graduate epidemiology course on the evaluative sciences we spend fully the first four to five weeks talking about how to develop a valid and answerable question. The cornerstone of this validity is its importance. Hence, the first question that we pose is: Is the study question important?

This is a bit of a loaded question, though. Important to whom? How is "important" defined? This is somewhat subjective, yet needs to be scrutinized nevertheless. In the context of an individual patient, the question may become: Is the study question important to me? So, importance is dependent on perspective. Nevertheless, there are questions upon whose importance we can all agree. For example, the importance of the question of whether our current fast-food life style promotes obesity and diabetes is hard to dispute.

Regardless of how we feel about the importance of the question, we must first identify the said research question. At least some of the time you will be able to find it in the primary paper, buried in the last paragraph of the Introduction section. Most of the questions we ask relate to etiologic relationships ("etiology" is medicalese for "causation"). Now, you have heard many times that an observational study cannot answer a causal question. Yet, why do we bother with the time, energy and money needed to run observational studies? Without getting too much into the weeds, philosophers of science tell us that no single study design can give us unequivocal evidence of causality. We can merely come close to it. What does this mean in practical terms? It means that, although most observational studies are still interested in causality rather than a mere association, we have to be more circumspect in how we interpret the results from such studies than from interventional ones. But I am jumping ahead.

Once we have identified and established the importance of the question, we need to evaluate its quality. A question of high quality is 1). clear, 2). specific, and 3). answerable. The question that I posed above regarding fast food and obesity possesses none of these characteristics. It is too broad and open to interpretation. If I were really posing a question in this vein, I would choose a single well defined exposure (consuming 3 cans of soda per day) influencing a single outcome (10% body weight gain) over a specific period of time (over 30 weeks). While this is a much narrower question that the one I proposed above, it is only by answering bundles of such narrow questions and putting the information together that we can arrive at the big picture.

A general principle that I like to teach to my student is the PICO or PECOT model (I did not come up with it, but am its avid user). In PICO, P=population, I=intervention or exposure, C=comparator, and O=outcome. The PECOT model is an adaptation of the PICO for observations over time, resulting in P=population, E=exposure, C=comparator, O=outcome, T=time. These models can help not only pose the question, but to unravel the often mysterious and far from transparent intent of the investigators.

Once you have identified the question and dealt with its importance, you are ready to move on to the next step: evaluating the study design as it relates to the question at hand. We will discuss this in the next post.

Read More

Guest post: How our brains are wired to advance science

We have a treat today. Today I am featuring a guest post from my brilliant 17-year-old niece Katherine Dana. She is currently applying to colleges, and this is one of her brief essays. Kathy is interested in animal communication specifically, but, as you can see, also spends a lot of time thinking about science in general. And oddly, she seems to be contemplating similar themes to the ones we address here. 
While it is hard for me to stop waxing poetic about how proud I am of her, I will now cut myself short, so that you can enjoy her lucid commentary.

By Katherine E. Dana

Marcel Proust once wrote, "The real voyage of discovery consists not in seeking new landscapes, but in having new eyes." Thus goes the song of science, humanity's great unifier. Science is not merely the means for collecting random information—it is the means through which we make sense of our world. It is messier than mathematics, less exact. And yet in some ways, it is this very inexactitude that gives science its potency, and allows it to cut to the very heart of nature's chaotic randomness. It works by taking the givens of nature and churning out elegant guesses, which predict as effectively as they describe.

One quality that distinguishes mind from machine is that leap of thought that psychologists term "heuristics"—mental shortcuts, expressly designed to help us connect the dots without having to consciously traverse the spaces between. This is our organic advantage.

While today's machines, no matter how complex, are restricted to lengthy algorithms, we may leap from branch to branch. Nowhere in human endeavors is this cognitive edge more apparent than in the combined efforts of humans seeking to find new truth. For before we can know, we must question; and this is where insight is most crucial. It is not enough to investigate the familiar. We must find the courage to ask uncomfortable questions, and be willing to uproot even our most cherished beliefs, all in the name of a deeper understanding.

Read More