Wednesday, January 27, 2016

"The Blizzard of 2016" and predictability: Part III: When is a health prediction 'precise' enough?

We've discussed the use of data and models to predict the weather in the last few days (here and here).  We've lauded the successes, which are many, and noted the problems, including people not heeding advice. Sometimes that's due, as a commenter on our first post in this series noted, to previous predictions that did not pan out, leading people to ignore predictions in the future.  It is the tendency of some weather forecasters, like all media these days, to exaggerate or dramatize things, a normal part of our society's way of getting attention (and resources).

We also noted the genuine challenges to prediction that meteorologists face.  Theirs is a science that is based on very sound physics principles and theory, that as a meteorologist friend put it, constrain what can and might happen, and make good forecasting possible.  In that sense the challenge for accuracy is in the complexity of global weather dynamics and inevitably imperfect data, that may defy perfect analysis even by fast computers.  There are essentially random or unmeasured movements of molecules and so on, leading to 'chaotic' properties of weather, which is indeed the iconic example of chaos, known as the so-called 'butterfly effect': if a butterfly flaps its wings, the initially tiny and unseen perturbation can proliferate through the atmosphere, leading to unpredicted, indeed, wildly unpredictable changes in what happens.
The Butterfly Effect, far-reaching effects of initial conditions; Wikipedia, source

Reducing such effects is largely a matter of needing more data.  Radar and satellite data are more or less continuous, but many other key observations are only made many miles apart, both on the surface and into the air, so that meteorologists must try to connect them with smooth gradients, or estimates of change, between the observations.  Hence the limited number of future days (a few days to a week or so) for which forecasts are generally accurate.

Meteorologists' experience, given their resources, provide instructive parallels as well as differences with biomedical sciences, that aim for precise prediction, often of things decades in the future, such as disease risk based on genotype at birth or lifestyle exposures.  We should pay attention to those parallels and differences.

When is the population average the best forecast?
Open physical systems, like the atmosphere, change but don't age.  Physical continuity means that today is a reflection of yesterday, but the atmosphere doesn't accumulate 'damage' the way people do, at least not in a way that makes a difference to weather prediction.  It can move, change, and refresh, with a continuing influx and loss of energy, evaporation and condensation, and circulating movement, and so on. By contrast, we are each on a one-way track, and a population continually has to start over with its continual influx of new births and loss to death. In that sense, a given set of atmospheric conditions today has essentially the same future risk profile as such conditions had a year or century or millennium ago. In a way, that is what it means to have a general atmospheric theory. People aren't like that.

By far, most individual genetic and even environmental risk factors identified by recent Big Data studies only alter lifetime risk by a small fraction.  That is why the advice changes so frequently and inconsistently.  Shouldn't it be that eggs and coffee either are good or harmful for you?  Shouldn't a given genetic variant definitely either put you at high risk, or not? 

The answer is typically no, and the fault is in the reporting of data, not the data themselves. This is for several very good reasons.  There is measurement error.  From everything we know, the kinds of outcomes we are struggling to understand are affected by a very large number of separate causally relevant factors.  Each individual is exposed to a different set or level of those factors, which may be continually changing.  The impact of risk factors also changes cumulatively with exposure time--because we age.  And we are trying to make lifetime predictions, that is, ones of open-ended duration, often decades into the future.  We don't ask "Will I get cancer by Saturday?", but "Will I ever get cancer?"  That's a very different sort of question.

Each person is unique, like each storm, but we rarely have the kind of replicable sampling of the entire 'space' of potentially risk-affecting genetic variants--and we never will, because many genetic or even environmental factors are very rare and/or their combinations essentially unique, they interact and they come and go.  More importantly, we simply do not have the kind of rigorous theoretical basis that meteorology does. That means we may not even know what sort of data we need to collect to get a deeper understanding or more accurate predictive methods.

Unique contributions of combinations of a multiplicity of risk factors for a given outcome means the effect of each factor is generally very small and even in individuals their mix is continually changing.  Lifetime risks for a trait are also necessarily averaged across all other traits--for example, all other competing causes of death or disease.  A fatal early heart attack is the best preventive against cancer!  There are exceptions of course, but generally, forecasts are weak to begin with and in many ways over longer predictive time periods they will simply approximate the population--public health--average.  In a way that is a kind of analogy with weather forecasts that, beyond a few days into the future, move towards the climate average.

Disease forecasts change peoples' behavior (we stop eating eggs or forego our morning coffee, say), each person doing so, or not, to his/her own extent.  That is, feedback from the forecast affects the very risk process itself, changing the risks themselves and in unknown ways.  By contrast, weather forecasts can change behavior as well (we bring our umbrella with us) but the change doesn't affect the weather itself.

Parisians in the rain with umbrellas, by Louis-Léopold Boilly (1803)

Of course, there are many genes in which variants have very strong effects.  For those, forecasts are not perfect but the details aren't worth worrying about: if there are treatments, you take them.  Many of these are due to single genes and the trait may be present at birth. The mechanism can be studied because the problem is focused.  As a rule we don't need Big Data to discover and deal with them.  

The epidemiological and biomedical problem is with attempts to forecast complex traits, in which most every instance is causally unique.  Well, every weather situation is unique in its details, too--but those details can all be related to a single unifying theory that is very precise in principle.  Again, that's what we don't yet have in biology, and there is no really sound scientific justification for collecting reams of new data, which may refine predictions somewhat, but may not go much farther.  We need to develop a better theory, or perhaps even to ask whether there is such a formal basis to be had--or is the complexity we see is just what there is?

Meteorology has ways to check its 'precision' within days, whereas biomedical sciences have to wait decades for our rewards and punishments.  In the absence of tight rules and ways to adjust errors, constraints on biomedical business as usual are weak.  We think a key reason for this is that we must rely not on externally applied theory, but internal comparisons, like cases vs controls.  We can test for statistical differences in risk, but there is no reason these will be the same in other samples, or the future.  Even when a gene or dietary factor is identified by such studies, its effects are usually not very strong even if the mechanism by which they affect risk can be discovered.  We see this repeatedly, even for risk factors that seemed to be obvious.

We are constrained not just to use internal comparisons but to extrapolate the past to the future.  Our comparisons, say between cases and controls, are retrospective and almost wholly empirical rather than resting on adequate theory.  The 
'precision' predictions we are being promised are basically just applications of those retrospective findings to the future.  It's typically little more than extrapolation, and because risk factors are complex and each person is unique, the extrapolation largely assumes additivity: that we just add up the risk estimates for various factors that we measured on existing samples, and use that sum as our estimate of future risk.  

Thus, while for meteorology, Big Data makes sense because there is strong underlying theory, in many aspects of biomedical and evolutionary sciences, this is simply not the case, at least not yet.  Unlike meteorology, biomedical and genetic sciences are the really harder ones!  We are arguably just as likely to progress in our understanding by accumulating results from carefully focused questions, where we're tracing some real causal signal (e.g., traits with specific, known strong risk factors), as by just feeding the incessant demands of the Big Data worldview.  But this of course is a point we've written (ranted?) about many times.

You bet your life, or at least your lifestyle!
If you venture out on the highway despite a forecast snowstorm, you are placing your life in your hands.  You are also imposing dangers on others (because accidents often involve multiple vehicles). In the case of disease, if you are led by scientists or the media to take their 'precision' predictions too seriously, you are doing something similar, though most likely mainly affecting yourself.  

Actually, that's not entirely true.  If you smoke or hog up on MegaBurgers, you certainly put yourself at risk, but you risk others, too. That's because those instances of disease that truly are strongly and even mappably genetic (which seems true of subsets of even of most 'complex' diseases), are masked by the majority of cases that are due to easily avoidable lifestyle factors; the causal 'noise' that risky lifestyles make genetic causation harder to tease out.

Of course, taking minor risks too seriously also has known potentially serious consequences, such as of intervening on something that was weakly problematic to begin with.  Operating on a slow-growing prostate or colon cancer in older people, may lead to more damage than the cancer will. There are countless other examples.

Life as a Garden Party
The need is to understand weak predictability, and to learn to live with it. That's not easy.

I'm reminded of a time when I was a weather officer stationed at an Air Force fighter base in the eastern UK.  One summer, on a Tuesday morning, the base commander called me over to HQ.  It wasn't for the usual morning weather briefing.....

"Captain, I have a question for you," said the Colonel.

"Yes, sir?"

"My wife wants to hold a garden party on Saturday.  What will the weather be?"

"It might rain, sir," I replied.

The Colonel was not very pleased with my non-specific answer, but this was England, after all!

And if I do say so myself, I think that was the proper, and accurate, forecast.**

Plus ça change..  Rain drenches royal garden party, 2013; The Guardian

**(It did rain.  The wife was not happy! But I'd told the truth.)


Anonymous said...

If epidemiology is a much a much harder problem than weather, then imagine how much harder it is to model, explain, and predict human social behavior. Perhaps the best we can do is parameterize our ignorance - and maybe even that is far too much to hope for. Much of behavior is likely formally indeterminable in causation, ultra-weak factors cannot be discerned above the noise, chance intervenes at every level, auto-causation stymies analysis, and irrationality reigns. Rival schools of thought (Darwinian, structuralist, Marxist...), all fatally flawed in their grandiose assurance to make sense of perceived patterns, share the equality of the grave.

Ken Weiss said...

There are many vested interests that don't like truth spoken. Social sciences have a poor record in this respect, in my view, which is similar to yours. The core problem isn't how difficult social factors and behavior are to understand, but rather the unwillingness to acknowledge that. Whether there are 'scientific' theories that can do the job, or some other way of understanding would be much better, it would be more productive to disconnect it from careerist needs. But that's not going to happen. Grand(iose) theories are very appealing to a species that likes tractable solutions to intractable problems. Even proving that something is indeterminate is a challenge. I'd rather try to forecast next week's weather....

Ken Weiss said...

A tweeter about this post seemed to think we were arguing that everything is genetic and that if environmental causes of disease were removed it would all fall out in the genetic wash. Of course, everything is going to have genetic components, but our point near the end of this post was different (and had a wording problem). It was that common complex diseases have been hard to map genetically. Many very small genotypic effects are found. But there may be a subset of truly 'genetic', such as single-gene, causation. Common risky behavior that leads to traits like obesity and diabetes and the like may mask genotypic effects that are strong but rare in the population, and hence hard to detect with the standard mapping methods. That subset of complex traits really is 'genetic' in the sense of strong, focal genetic causation. Reduce the fog of environmental causes, and these important subsets might be much easier to find and ultimately do something about.

Bill R said...

A small comment:

You said: "By far, most individual genetic and even environmental risk factors identified by recent Big Data studies only alter lifetime risk by a small fraction" Well, that's the idea. We already know most of the easy ones. For example, I've known about my atypical butyrylcholinesterase for almost 55 years. It's simple. Give me the right anesthetic, I stop breathing and die (maybe). Organophosphate exposure causes all sorts of problems. That is a fairly big effect (N=1) and doesn't require "Big Data." The purpose of Big Data epidemiology, and of most statistics is to identify smaller effects for followup. The easy ones have already been discovered.

Also, chaotic effects don't go away with more data and increase with prediction lengths.

Ken Weiss said...

There are roles for all sorts of approaches. Most of the justification for Big Data genomics is about addressing common complex diseases that are affected by a plethora of individually small effects that are in many ways ephemeral and only work in combination with countless others, and usually also related to lifestyle exposures. Rare severe disorders don't generally require mapping kinds of approaches. If you've known about your trait for 55 years, that proves the point. But there is another reason for my particular view, and that is that there are many clearly known disease-causing factors, mostly environmental that are much more important. The regular recurrence of newly documented infectious disease problems are in addition.

How one should approach very rare, but strong-effect genetic causation is something worth discussing, but (to me) not just an excuse for lobbying for larger, longer massively induction-based projects that once started typically become politically impossible to stop once diminishing returns have set in. There are a few instances where they have been ended, but the general lesson is that they won't. In a sense, it's a matter of priorities, and hence subjectivity, I guess.