Is consistency trivial in randomized controlled trials?

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Here are some more thoughts on Hernan and Taubman’s famous 2008 paper, from a chapter I am finalising for the epidemiology entry in a collection on the philosophy of medicine. I realise I have made a similar point in an earlier post on this blog, but I think I am getting closer to a crisp expression. The point concerns the claimed advantage of RCTs for ensuring consistency. Thoughts welcome!

Hernan and Taubman are surely right to warn against too-easy claims about “the effect of obesity on mortality”, when there are multiple ways to reduce obesity, each with different effects on mortality, and perhaps no ethically acceptable way to bring about a sudden change in body mass index from say 30 to 22 (Hernán and Taubman 2008, 22). To this extent, their insistence on assessing causal claims as contrasts to well-defined interventions is useful.

On the other hand, they imply some conclusions that are harder to accept. They suggest, for example, that observational studies are inherently more likely to suffer from this sort of difficulty, and that experimental studies (randomized controlled trials) will ensure that interventions are well-specified. They express their point using the technical term “consistency”:

consistency… can be thought of as the condition that the causal contrast involves two or more well-defined interventions. (Hernán and Taubman 2008, S10)

They go on:

…consistency is a trivial condition in randomized experiments. For example, consider a subject who was assigned to the intervention group … in your randomized trial. By definition, it is true that, had he been assigned to the intervention, his counterfactual out- come would have been equal to his observed outcome. But the condition is not so obvious in observational studies. (Hernán and Taubman 2008, s11)

This is a non-sequitur, however, unless we appeal to a background assumption that an intervention—something that an actual human investigator actually does—is necessarily well-defined. Without this assumption, there is nothing to underwrite the claim that “by definition”, if a subject actually assigned to the intervention had been assigned to the intervention, he would have had the outcome that he actually did have.

Consider the intervention in their paper, one hour of strenuous exercise per day. “Strenuous exercise” is not a well-defined intervention. Weightlifting? Karate? Swimming? The assumption behind their paper seems to be that if an investigator “does” an intervention, it is necessarily well-defined; but on reflection this is obviously not true. An investigator needs to have some knowledge of which features of the intervention might affect the outcome (such as what kind of exercise one performs), and thus need to be controlled, and which don’t (such as how far west of Beijing one lives). Even randomization will not protect against confounding arising from preference for a certain type of exercise (perhaps because people with healthy hearts are predisposed both to choose running and to live longer, for example), unless one knows to randomize the assignment of exercise-types and not to leave it to the subjects’ choice.

This is exactly the same kind of difficulty that Hernan and Taubman press against observational studies. So the contrast they wish to draw, between “trivial” consistency in randomized trials and a much more problematic situation in observational studies, is a mirage. Both can suffer from failure to define interventions.

Workshop, Helsinki: What do diseases and financial crises have in common?

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AID Forum: “Epidemiology: an approach with multidisciplinary applicability”

(Unfamiliar with AID forum? For the very idea and the programme of Agora for Interdisciplinary Debate, see www.helsinki.fi/tint/aid.htm)

DISCUSSED BY:

Mervi Toivanen (economics, Bank of Finland)

Jaakko Kaprio (genetic epidemiology, U of Helsinki)

Alex Broadbent (philosophy of science, U of Johannesburg)

Moderated by Academy professor Uskali Mäki

Session jointly organised by TINT (www.helsinki.fi/tint) and the Finnish Epidemiological Society (www.finepi.org)

TIME AND PLACE:

Monday 9 February, 16:15-18

University Main Building, 3rd Floor, Room 5

http://www.helsinki.fi/teknos/opetustilat/keskusta/f33/ls5.htm

TOPIC: What do diseases and financial crises have in common?

Epidemiology has traditionally been used to model the spreading of diseases in populations at risk. By applying parameters related to agents’ responses to infection and network of contacts it helps to study how diseases occur, why they spread and how one could prevent epidemic outbreaks. For decades, epidemiology has studied also non-communicable diseases, such as cancer, cardiovascular disease, addictions and accidents. Descriptive epidemiology focuses on providing accurate information on the occurrence (incidence, prevalence and survival) of the condition. Etiological epidemiology seeks to identify the determinants be they infectious agents, environmental or social exposures, or genetic variants. A central goal is to identify determinants amenable to intervention, and hence prevention of disease.

There is thus a need to consider both reverse causation and confounding as possible alternative explanations to a causal one. Novel designs are providing new tools to address these issues. But epidemiology also provides an approach that has broad applicability to a number of domains covered by multiple disciplines. For example, it is widely and successfully used to explain the propagation of computer viruses, macroeconomic expectations and rumours in a population over time.

As a consequence, epidemiological concepts such as “super-spreader” have found their way also to economic literature that deals with financial stability issues. There is an obvious analogy between the prevention of diseases and the design of economic policies against the threat of financial crises. The purpose of this session is to discuss the applicability of epidemiology across various domains and the possibilities to mutually benefit from common concepts and methods.

QUESTIONS:

1. Why is epidemiology so broadly applicable?

2. What similarities and differences prevail between these various disciplinary applications?

3. What can they learn from one another, and could the cooperation within disciplines be enhanced?

4. How could the endorsement of concepts and ideas across disciplines be improved?

5. Can epidemiology help to resolve causality?

READINGS:

Alex Broadent, Philosophy of Epidemiology (Palgrave Macmillan 2013)

http://www.palgrave.com/page/detail/?sf1=id_product&st1=535877

Alex Broadbent’s blog on the philosophy of epidemiology:

https://philosepi.wordpress.com/

Rothman KJ, Greenland S, Lash TL. Modern Epidemiology 3rd edition.

Lippincott, Philadelphia 2008

D’Onofrio BM, Lahey BB, Turkheimer E, Lichtenstein P. Critical need for family-based, quasi-experimental designs in integrating genetic and social science research. Am J Public Health. 2013 Oct;103 Suppl 1:S46-55. doi:10.2105/AJPH.2013.301252.

Taylor, AE, Davies, NM, Ware, JJ, Vanderweele, T, Smith, GD & Munafò, MR 2014, ‘Mendelian randomization in health research: Using appropriate genetic variants and avoiding biased estimates’. Economics and Human Biology, vol 13., pp. 99-106

Engholm G, Ferlay J, Christensen N, Kejs AMT, Johannesen TB, Khan S, Milter MC, Ólafsdóttir E, Petersen T, Pukkala E, Stenz F, Storm HH. NORDCAN: Cancer Incidence, Mortality, Prevalence and Survival in the Nordic Countries, Version 7.0 (17.12.2014). Association of the Nordic Cancer Registries. Danish Cancer Society. Available from http://www.ancr.nu.

Andrew G. Haldane, Rethinking of financial networks; Speech by Mr Haldane, Executive Director, Financial Stability, Bank of England, at the Financial Student Association, Amsterdam, 28 April 2009: http://www.bis.org/review/r090505e.pdf

Antonios Garas et al., Worldwide spreading of economic crisis: http://iopscience.iop.org/1367-2630/12/11/113043/pdf/1367-2630_12_11_113043.pdf

Christopher D. Carroll, The epidemiology of macroeconomic expectations: http://www.econ2.jhu.edu/people/ccarroll/epidemiologySFI.pdf

Two recent papers

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I’ve had two papers come out this month (/year!):

‘Risk Relativism and Physical Law’ in Journal of Epidemiology and Community Health – http://jech.bmj.com/content/69/1/92?etoc

‘Disease as a theoretical concept: The case of HPV-itis’ in Studies in History and Philosophy of Biological and Biomedical Sciences – http://www.sciencedirect.com/science/article/pii/S1369848614000910

Interventionism vs contrastivism about causation

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I am trying to summarise (for a paper to be submitted to an epidemiology journal) the differences and similarities between interventionism (a la Woodward, Hitchcock) and contrastivism (a la Schaffer, perhaps Menzies) about causation. Are the following comments fair, and fairly comprehensive?

1) The two approaches share the view that the significance of causal claims is relative to some specified scenario in which some other specified event occurs rather than the cause (unlike pure counterfactual theories, which do not require any such specification).

2) Contrastivism (a la Schaffer) is primarily a semantic thesis about the meaning of “cause” and cognates. Specifically, contrastivism is committed to a descriptive thesis about ordinary talk, namely that “C causes E” implies “C rather than C* causes E rather than E*”, while interventionism is not committed to this (though compatible with it).

3) Interventionism seeks to explicate or shed light on characteristic patterns of counterfactual dependence that surround the instantiation of causes (without fully reducing causation to counterfactual dependence). Contrastivism does not seek to do this (though is compatible with doing so).

4) Both accounts are amenable to “cause” being left ultimately unanalysed, and proponents of each (e.g. Schaffer, Woodward) have expressed pessimism about the prospects of reducing causes to counterfactual dependence.

Comments/thoughts welcome. Thanks.

Is the Methodological Axiom of the Potential Outcomes Approach Circular?

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Hernan, VanderWeele, and others argue that causation (or a causal question) is well-defined when interventions are well-specified. I take this to be a sort of methodological axiom of the approach.

But what is a well-specified intervention?

Consider an example from Hernan & Taubman’s influential 2008 paper on obesity. In that paper, BMI is shown up as failing to correspond to a well-specified intervention; better-specifed interventions include one hour of strenuous physical exercise per day (among others).

But what kind of exercise? One hour of running? Powerlifting? Yoga? Boxing?

It might matter – it might turn out that, say, boxing and running for an hour a day reduce BMI by similar amounts but that one of them is associated with longer life. Or it might turn out not to matter. Either way, it would be a matter of empirical inquiry.

This has two consequences for the mantra that well-defined causal questions require well-specified interventions.

First, as I’ve pointed out before on this blog, it means that experimental studies don’t necessarily guarantee well-specified interventions. Just because you can do it doesn’t mean you know what you are doing. The differences you might think don’t matter might matter: different strains of broccoli might have totally different effects on mortality, etc.

Second, more fundamentally, it means that the whole approach is circular. You need a well-specified intervention for a good empirical inquiry into causes and you need good empirical inquiry into causes to know whether your intervention is well-specified.

To me this seems to be a potentially fatal consequence for the claim that well-defined causal questions require well-specified interventions. For if that were true, we would be trapped in a circle, and could never have any well-specified interventions, and thus no well-defined causal questions either. Therefore either we really are trapped in that circle; or we can have well-defined causal questions, in which case, it is false that these always require well-specified interventions.

This is a line of argument I’m developing at present, inspired in part by Vandebroucke and Pearce’s critique of the “methodological revolution” at the recent WCE 2014 in Anchorage. I would welcome comments.

Causation, prediction, epidemiology – talks coming up

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Perhaps an odd thing to do, but I’m posting the abstracts of my two next talks, which will also become papers. Any offers to discuss/read welcome!

The talks will be at Rhodes on 1 and 3 October. I’ll probably deliver a descendant of one of them at the Cambridge Philosophy of Science Seminar on 3 December, and may also give a very short version of 1 at the World Health Summit in Berlin on 22 Oct.

1. Causation and Prediction in Epidemiology

There is an ongoing “methodological revolution” in epidemiology, according to some commentators. The revolution is prompted by the development of a conceptual framework for thinking about causation called the “potential outcomes approach”, and the mathematical apparatus of directed acyclic graphs that accompanies it. But once the mathematics are stripped away, a number of striking assumptions about causation become evident: that a cause is something that makes a difference; that a cause is something that humans can intervene on; and that epidemiologists need nothing more from a notion of causation than picking out events satisfying those two criteria. This is especially remarkable in a discipline that has variously identified factors such as race and sex as determinants of health. In this talk I seek to explain the significance of this movement in epidemiology, separate its insights from its errors, and draw a general philosophical lesson about confusing causal knowledge with predictive knowledge.

2. Causal Selection, Prediction, and Natural Kinds

Causal judgements are typically – invariably – selective. We say that striking the match caused it to light, but we do not mention the presence of oxygen, the ancestry of the striker, the chain of events that led to that particular match being in her hand at that time, and so forth. Philosophers have typically but not universally put this down to the pragmatic difficulty of listing the entire history of the universe every time one wants to make a causal judgement. The selective aspect of causal judgements is typically thought of as picking out causes that are salient for explanatory or moral purposes. A minority, including me, think that selection is more integral than that to the notion of causation. The difficulty with this view is that it seems to make causal facts non-objective, since selective judgements clearly vary with our interests. In this paper I seek to make a case for the inherently selective nature of causal judgements by appealing to two contexts where interest-relativity is clearly inadequate to fully account for selection. Those are the use of causal judgements in formulating predictions, and the relation between causation and natural kinds.

JOB: Post Doc – prediction, philosophy of epidemiology, philosophy of science

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The Department of Philosophy at the University of Johannesburg seeks to appoint a postdoctoral research fellow to work under the supervision of Prof Alex Broadbent. In particular, ideas for work on (1) prediction or (2) philosophy of epidemiology are welcome; but any area of speciality within the philosophy of science broadly construed (including the philosophy of medicine) will be considered. Please send a CV, cover letter, and writing sample to abbroadbent@uj.ac.za by 26 September 2014. PhD must be in hand. Start date February 2014. Informal inquiries welcome to the same email address.

A Tale of Two Papers

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I’m on my way back from the World Epi Congress in Anchorage, where causation and causal inference have been central topics of discussion. I wrote previously about a paper (Hernan and Taubman 2008) suggesting that obesity is not a cause of mortality. There is another, more recent paper published in July of this year, suggesting, more or less, that race is not a cause of health outcomes – or at least that it’s not a cause that can feature in causal models (Vanderweele and Robinson 2014). I can’t do justice to the paper here, of course, but I think this is a fair, if crude, summary of the strategy.

This paper is an interesting comparator for the 2008 obesity paper (Hernan and Taubman 2008). It shares the idea that there is a close link between (a) what can be humanly intervened on, (b) what counterfactuals we can entertain, and (c) what causes we can meaningfully talk about. This is a radical view about causation, much stronger than any position held by any contemporary philosopher of whom I’m aware. Philosophers who do think that agency or intervention are central to the concept of causation treat the interventions as in-principle ones, not things humans could actually do.

Yet feasibility of manipulating a variable really does seem to be a driver in this literature. In the paper on race, the authors consider what variables form the subject of humanly possible interventions, and suggest that rather than ask about the effect of race, we should ask what effect is left over after these factors are modelled and controlled for, under the umbrella of socioeconomic status. That sounds to me a bit like saying that we should identify the effects of being female on job candidates’ success by seeing what’s left after controlling for skirt wearing, longer average hair length, shorter stature, higher pitched voice, female names, etc. In other words, it’s very strange indeed. Perhaps it could be useful in some circumstances, but it doesn’t really get us any further with the question of interest – how to quantify the health effects of race, sex, and so forth.

Clearly, there are many conceptual difficulties with this line of reasoning. A good commentary was published with the paper (Glymour and Glymour 2014) which really dismantles the logic of the paper. But I think there are a number of deeper and more pervasive misunderstandings to be cleared up, misunderstandings which help explain why papers like this are being written at all. One is confusion between causation and causal inference; another is confusion between causal inference and particular methods of causal inference; and a third is a mix-up between fitting your methodological tool to your problem, and your problem to your tool.

The last point is particularly striking. What’s so interesting about these two papers (2008 & 2014) is that they seem to be trying to fit research problems to methods, not trying to develop methods to solve problems – even though this is ostensibly what they (at least VW&R 20114) are trying to do. To me, this is strongly reminiscent of Thomas Kuhn’s picture of science, according to which an “exemplary” bit of science occurs, and initiates a “paradigm”, which is a shared set of tools for solving “puzzles”. Kuhn was primarily influenced by physics, but this way of seeing things seems quite apt to explain what is otherwise, from the outside, really quite a remarkable, even bizarre about-turn. Age, sex, race – these are staple objects of epidemiological study as determinants of health; and they don’t fit easily into the potential outcomes paradigm. It’s fascinating to watch the subsequent negotiation. But I’m quite glad that it doesn’t look like epidemiologists are going to stop talking about these things any time soon.

References

Glymour C and Glymour MR. 2014. ‘Race and Sex Are Causes.’ Epidemiology 25 (4): 488-490.

Hernan M and Taubman S. 2008. ‘Does obesity shorten life? The importance of well-defined interventions to answer causal questions.’ International Journal of Obesity 32: S8–S14.

VanderWeele TJ and Robinson WR. 2014. ‘On the Causal Interpretation of Race in Regressions Adjusting for Confounding and Mediating Variables.’ Epidemiology 25(4): 473-484.

Snakes, statistics, and goals for the goal-setters

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Cesar Victora gave a very interesting talk earlier today concerning the International Epidemiology Association’s position paper on the UN’s Sustainable Development Goals, which are currently being drafted (to replace the Millennium Development Goals post-2015). Victora is President of the IEA, for a few more hours at least (the new President takes office this evening). Many of his points were reiterated by the next speaker, Theodor Abelin, and in questions from the floor. There were no audible voices of dissent. (The talk reflects a fuller position paper, available here.)

The point that stayed with me most from Victora’s rich talk was the importance of relating goals to appropriate measurement techniques. My own interest in epidemiology has tended to focus on efforts to identify causes (“analytic” epidemiology), since causation is a natural magnet for philosophical interest. But measurement is also a focus of philosophical interest, and Victora nicely pointed out that “descriptive” epidemiology – the business of measuring things like maternal mortality rate, for example – is extremely important if these Sustainable Development Goals are to be effective. A country cannot be held to a goal that cannot be measured, and it cannot be fairly be held to a goal when progress towards that goal is estimated rather than measured.

For example, I was not surprised to learn that in many countries where maternal mortality is high, data on maternal mortality rates (MMRs) are scarce. What did surprise me was hearing about the calculations that some august international organisations perform in the absence of data. A calculation is performed involving GDP per capita, general fertility rate and skilled birth attendance. MMR is estimated as a function of these and perhaps some other similar variables. This means that if the country goes through a recession, the estimated MMR will automatically go up. – Perhaps is really will go up, but it seems strange to think of that calculation as a measurement, at least in the absence of extremely good evidence for the reliability of the estimating equation – evidence which, of course, we don’t have.

MMR is measurable, of course. The problem with MMR is simply a lack of data, and this problem afflicts a large class of conditions. As Victora put it in relation to snakebite: “Where we have snakes, we don’t have statistics, and where we have statistics, we don’t have snakes.”

However, Victora’s most penetrating critique of the SDGs concerned the setting of goals in the absence of clear ideas about how progress towards the goals will be measured. The health-related goal is as follows:

“Goal 3. Ensure healthy lives and promote well-being for all at all ages” (from the Outcome Document)

This overarching goal is broken down into 13 subgoals, some of which are very loosely specified. For instance, how are we to tell whether a country has managed to “strengthen prevention and treatment of substance abuse, including narcotic drug abuse and harmful use of alcohol”? Ironically, those goals that are most clearly specified are wildly unattainable, such as halving global deaths and injuries from road traffic accidents by 2020. Those that are not well specified present measurement challenges for epidemiologists.

This made me wonder whether a body like the IEA could itself set some “goals for the goal-setters” – that is, criteria which any health-related goal must meet if, in the professional opinion of the IEA, they are to be useful. The simplest such criterion would be that outcomes must be specified in terms of a recognised epidemiological measure (mortality, for instance). Another might be to accompany each goal with information (perhaps in a corresponding entry in an appendix) concerning the trend over the past similar period: so if the goal is the halve road traffic deaths in 15 years, or 25, information on the growth of road traffic deaths over the past 15 or 25 years might be included. Goals of this kind will always be political, but there might be agreement on a set of simple rules for setting such goals, and if such rules existed, this might pull epidemiologists closer in to the goal-setting process – a kind of politicking which, as one of the questioners pointed out, is not part of standard epidemiological training.