Basic Sciences
All scientific disciplines have some interplay between what
is accepted as established reality and speculative theory. As might be supposed, the most metaphysical
of sciences is physics. The particles
and waves that are subject of study are also the building material for
chemistry, biology, and all the many macrosciences. If physics isn’t real, then what is? Furthermore, the development of new knowledge
in physics is uncommon, which often makes a degree in physics tantamount to a
degree in physical engineering. Speculation
about your navel will never change its shape.
Epidemiology is at the opposite end of the spectrum. It is the most epistemological science. Nothing is quite what it seems, perhaps. An association may be attributable to a
butterfly flapping its wings on the other side of the world. If physical theories are superseded every
century or so, epidemiological theories are apt to be trumped sometime next
month. So, compared to physics, epidemiology
is a terrible and highly unreliable science.
But, the thing is, the subject matter of epidemiology is often more
important. So, when done well, epidemiology
is very useful and informative. On the
other hand, when done badly, voodoo and astrology look far better. The trick is this: The scientific methodology
underlying good epidemiology bears scant resemblance to that of physics.
Peer Review
When the premises are not in dispute, a few knowledgeable judges
can assure that the conclusions are correct.
The earth is round. Oranges are
round. The circumference of a circle is π
times the diameter. The surface area of
an orange peel is three times π times the radius squared. So,
orange peels are easy. On the other
hand, apples are not quite so round. As
a result, the judges of apple peel experiments are not quite so indisputable as
are the orange peel judges. And besides,
the round earth just might be a government plot. After all, just whom do the judges work for?
When epistemology rules the roost, the expectation that
academic journals can guarantee the veracity of what appears on ink and paper has
little foundation. Yes, a reviewer can
be expected to employ the same premises as the author, but if those assumptions
are incorrect then the game is off, or at least it is different. Instead of a few experts, there are many who
may harbor an alternative theory that is, in fact, quite plausible. Common sense is, or at least some of it, is as
reliable as expert judgment.
In physics, one plausible explanation is a pretty good
trick. In fact, figuring out what needs
to be explained is pretty much the whole story.
When Newton “discovered” gravity, it came by noting that masses attract. Working out the math was simple after
that. When Newtonian mechanics was
supplanted by quantum mechanics, it happened by changing what was being
explained. The reality explained by
epidemiology is far more speculative. The
influences that may affect the occurrence of a chronic disease are often
numerous, and every one posited is a theoretical explanation of why, at least
in part, the disease occurs. To make
matters even more complicated, the different explanations may draw on many
different other sciences. There may be
toxicological explanations, nutritional explanations, and socioeconomic
explanations with their own underlying biology.
Therefore, unless they are all unusually accomplished scientists with a very
broad range of interests, a narrowly defined group of “epidemiological” peers
are not going to be adequate for judging how good all the competing explanation
really are. For example, the wrong set
of peers might approve of the use of a regression analysis with log-transformed
dose.
The Tyranny of the Journal Article
Publish or perish.
Generate new knowledge now or get out of town. That’s a quasi-reasonable performance standard
for physics or any science with a high fact-to-theory ratio. But history and epidemiology move too slowly and
unpredictably for that. Recording
history is at least as important as drawing conclusions from it. Similarly, the results from the single study
will soon be less notable than the results of the meta-analysis that will ensue
when merit is given to the initial conclusions. Therefore, unless the association is very
strong, analyzing the results from a single cohort is hardly worth doing. Yet, dumping the data into the statistical
significance hopper is considered to be an essential part of a career in
epidemiology.
If a study is well designed in the first place, the most
valuable result is the data. Collecting
what is often very similar data just so a new analysis can be conducted is silly. In fact, collecting data and analyzing could
easily be dissociated into separate efforts that could be funded separately. A grant given to an investigator to collect
data and “publish” it in a public repository is money well spent. A grant to collect data that is hidden or
thrown away after a single analysis is not.
Yes, data collection should be hypothesis driven, but it should not be
presumed that the hypothesis a particular investigator has in mind is the only
one that the data may be useful for.
The analysis of pooled data from multiple studies by
Lanphear et al (2005) represents a very rare good example of what is possible
when data is shared. However, the
investigators from the different studies only agreed to share the data among
themselves. This is especially
unfortunate because the analysis primarily relied on a loglinear dose-response
model. When a group of World Health Organzation
toxicologists (2011) relied on this analysis, they were obliged to produce a
dose-response model from adjusted data, rather than actual data.
Disease Models
Many of the health outcomes that are the subject of
environmental epidemiology are common and are known to be influenced by many
variables. Instead of a new multivariate
regression analysis for every study, adjusting for “confounding variables” could
be done much more reliably it the adjustments were based on literature
collected over many decades and many cohorts.
That would reduce the possibility of a non-causal cohort-specific association,
and would encourage more careful consideration of the quantitative cause-effect
relationship of all the variables. The
rule of thumb is that a relative risk of at least two is needed to exclude a
strong possibility of an association that is not causally related. But with pooled data and reliable quantitative
models for major disease factors, the possibility of quantitative characterization
of lesser causal influences would be greatly enhanced.
For example, let’s suppose a study is being developed to characterize
the relationship between arsenic and lung cancer. The main influence on the development of lung
cancer is smoking, and therefore, detailed knowledge about the quantitative relationship
between smoking and lung cancer would greatly aid the determination of what
additional effect arsenic has, and how it does or does not interact with
smoking. But without a long history of
recorded observation, that is a pipe dream.
References
Lanphear BP, et al (2005).
Low-level environmental lead exposure and children's intellectual
function: an international pooled analysis.
Environ Health Perspect. 113:894-9.
World Health Organisation (2011). Lead (addendum). Safety
evaluation of certain food additives and contaminants. WHO Food Additive Series 44, pp 381-497.
Official Post Soundtrack
Post Notes
Thesis #32. One more on epidemiology will finish the thread, I think.
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