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EBM+ in The Reasoner
The Reasoner is a monthly digest highlighting exciting new research on reasoning, inference and method broadly construed. It is interdisciplinary, covering research in, e.g., philosophy, logic, AI, statistics, cognitive science, law, psychology, mathematics and the sciences. In this month’s issue you can find a report on the workshop held at the University of Kent last month on Evidence of mechanisms in evidence-based medicine.
End of the year
It’s almost the last workday of 2014 for me today, and a busy last few weeks means that I’m looking forward to the Christmas break. All things being well, I’ll be able to spend some quality time with two books that I’ve been meaning to read for ages.
Some holiday reading…
Götzsche, Peter. 2013. Deadly Medicines and Organised Crime. Radcliffe.
The first of these is not, I’m afraid, cheerful holiday reading. In fact, if you’d like a brief summary of the most important bit of Gotzsche’s argument, the title of a recent paper – Our prescription drugs kill us in large numbers – tells you most of what you need to know. The book follows on from this: page 1 begins with the quote “drugs are the third leading cause of death after heart disease and cancer.”
This is shocking stuff, which becomes even more troubling because of the lucid way that Götzsche lays out his evidence in support. The mechanisms by which drugs became so dangerous are largely down to the pharmaceutical industry. I’m planning a proper review of the book for some time in the new year, but by way of a taster, Götzsche argues that drug companies are largely free to mislead clinicians about both the safety and efficacy of drugs, and that this impunity has largely come about through a shortfall of regulatory oversight.
This brings me to my second lump (affectionately!) of holiday reading, which deals with the big-daddy of the pharma regulators: the FDA. Götzsche argues that the FDA has become an environment that is completely subordinate to the drug industry. But I think that there might be more to say on the historical side here. Luckily, so did Daniel Carpenter, whose book deals with just that.
Carpenter, Daniel. 2014. Reputation and power: organizational image and pharmaceutical regulation at the FDA. Princeton University Press.
This book came out of the FDA project at Harvard. This seems surprising to me, given that the book itself was published by a different Ivy League press. But no matter what intrigue may have lead to this route of publication, I’m told that is a startlingly thorough and penetrating account of how the FDA came to be. Until I’ve spent much more time with it, though, I’ll hold off trying to summarise 700-odd pages of detailed argument.
A New Year’s resolution
Whatever you think of Peter Götzsche’s overall analogies between the pharmaceutical industry and members of the Soprano family, I think that trials conducted in secret have to be of concern. That’s why I’m also making a new-year’s resolution to be more vocal in my support of the AllTrials campaign. You can find out more on their website, but the least that you need to know is that…
AllTrials calls for all past and present clinical trials to be registered and their full methods and summary results reported.
I’m signing their petition, and urge you to sign too, largely because finding things out in medicine is already hard enough without concealed trial results.
What #environment in disease aetiology?
Scienza in Rete is an Italian online magazine that popularises scientific research and also discusses various issues related to science and society, including science policy and ethics. It recently featured an article on possible causes of autism. Autism is a disease affecting the individual’s abilities to interact, verbally and non verbally, with other people. Its aetiology still has grey areas and much research is needed to understand its mechanisms.
I was intrigued by the aforementioned article because the title includes ‘ambiente’ – the environment. As you go through it, though, it becomes clear that the environment has a quite specific, restricted sense: the chemicals to which an individual is exposed, whether in life or even in utero. To be sure, this is precisely what environmental epidemiology investigates. The study mentioned in the ‘Scienza in Rete’ article evaluates, specifically, exposure to methylmercury. Fair enough. It is of utmost importance to understand the total exposome, and there is excellent research in progress in this respect.
But can chemicals exhaust all there is about the environment? What about the social environment?
Biological (or, biochemical) and social (or, socio-economic, psychological, behavioural) causes of disease shouldn’t be studied separately. We should instead strive to understand how the biological and the social realms interact. We should work towards integration of disease aetiologies and try to understand the mixed mechanisms of diseases. It is about time to move beyond the biologisation of disease and return to a more holistic understanding.
New knowledge and what to measure
I have been struck this week about how issues to do with evidence of mechanism have arisen in all my classes, and how students from multiple scientific backgrounds have set about attempting to understand evidence in science in very different ways.
On my masters course, students studying causality were particularly looking at evidential pluralism in health, at the idea that there were many ways in which evidence of correlation even from well-conducted large-scale observational studies or randomised controlled trials could mislead us, and evidence of mechanism could be used as a useful complement.
My masters students come from backgrounds in medicine, biochemistry, geology and social science, and were struck by what a complicated integrated thing evidence of causality is. Discussion ranged over issues that cross different sciences, before homing in on the question, pertinent to all trials, of what to measure and how to measure it.
We began with looking at historical Medical Research Council trials of streptomycin, and why they chose, during the trials, to study X-rays of patients’ lungs, and also to test bacteria in the treatment group to see whether they were developing resistance to streptomycin over the course of the 4 months of treatment. It is difficult to see how such choices could be made except in terms of a background theory of the mechanism of action of both disease and cure. A student was arguing, further, that such choices are always contextual, because there is never going to be a one-size-fits-all story for when *no evidence of mechanism* counts as *evidence of no mechanism* – for when you have looked well enough for a hypothesised mechanism to be sure that it is not there.
Discussion then ranged to more recent attempts to investigate the social determinants of health, including claims such as: socioeconomic status causes ill health, and stress causes ill health. Another student was asking how we should conceive of such questions, and pointing out that we try to investigate such claims by studying how lower-level variables, like housing, education, and so on cause ill-health. The student argued that we often look at the same or very similar low-level variables to try to understand how these apparently different social factors could cause ill health – and this may well be because we don’t know what the mechanism is. The idea developed by the class was that there are always going to be cases where – when we don’t know – the process of coming to understand mechanisms will be intertwined with the process of deciding what variables to measure when looking for correlations.
On the next day, there was a class of 3rd year students studying philosophy of natural sciences, coming to the class from science and technology studies, physics, medicine, biochemistry, social sciences and philosophy. We were discussing data science and curation practices, including visualisation techniques that let you assess data. I was struck by a unifying dream here, and in some approaches to medical evidence, of data-driven science, objective and without bias.
The class examined Leonelli’s discussion of the various ways in which embodied knowledge of the target material, technologies and practices of the disciplines studying, for example, Arabidopsis, are essential to getting big interactive, interdisciplinary databases of data on Arabidopsis to work.
Again, students noticed how important our theoretical understanding, alongside practical expertise, is in allowing us to build and use technology, and choose the data points to put into databases – including choosing how to tag data with metadata about its origins, so allowing data to ‘travel’, in Leonelli’s evocative terminology.
So, in a certain sense these two classes were concerned with exactly the same problem: until we have some theoretical and possibly also practical understanding – that comes in medicine from understanding mechanisms – we will not converge even on the basic data-points, the basic variables to measure, to choose to put into our databases or our study design in order even to begin data-driven science.
A final thank-you to my students for a very interesting week!
References
Sabina Leonelli: Volume 22, Issue 3 – Fall 2014 – Special Issue on Simulation, Visualization, and Scientific Understanding: Henk W. De Regt and Wendy S. Parker, Guest Editors