>> In this module, we will discuss the types
of epidemiologic studies and their strengths and weaknesses.
We will also investigate common mistakes
in epidemiologic studies.
Let's start with a review of the types of epidemiologic studies.
There are 4 general types.
Each one has intrinsic quality because of their design
and each one is susceptible to study biases
and confounding influences
that can distort findings and produce errors.
Perhaps 99% of radiation epidemiology studies are
non-experimental, and are called observational studies.
In observational studies, you play the hand you're dealt.
You must deal with factors, often unknown,
that distort study findings
and cause spurious, or false results.
These include bias in the design that cannot be controlled
in the analysis, and confounding factors,
such as cigarette smoking.
Some studies are more susceptible to bias,
such as the ecologic or correlation ones,
and some are less susceptible,
such as the cohort or follow-up ones.
First in quality, but perhaps least informative,
are experimental studies.
There are few radiation experiments
on humans as you might imagine.
Randomized clinical trials, however, are human experiments
where patients are randomly assigned
into radiation-exposed groups and non-radiation exposed groups
and then evaluated for differences in cancer survival.
Only in rare circumstances do they provide quantitative
information on radiation health risks.
They just aren't designed to do so.
Cohort studies or follow-up studies usually have the highest
intrinsic quality of the observational studies.
You start with a population that's exposed
and then follow them for the rest of their lives
or as long as possible.
You seek to determine what cancers
or diseases they develop.
Exposures and doses are determined before the cancers
have occurred, so they are least susceptible to many
of the biases seen in other study designs.
Concerns are selection bias as to who gets chosen for study,
screening bias if the high dose subjects are more intensely
screened for disease than the low dose subjects,
ascertainment bias if large numbers are lost to follow-up
and then confounding factors.
Case-control studies start with a disease
and then the exposure is determined.
For example, all mothers with children who died from cancer
in a well-defined population, such as Great Britain,
might be asked whether they received abdominal x-rays
while pregnant.
For each mother in the subject or case group, you find a mother
of similar age when giving birth and in the same calendar period,
living in the same locality, but whose child did not die.
The control mother is asked the same question.
For those who responded, and response rate is a concern,
you compare the responses
and interpret any differences that are found.
This type of study is more susceptible to bias
because results depend on who agrees to participate,
memory recall, response bias and other factors.
In this example, the mothers of children
who have died might be more reliable responders
than control mothers of living children
because of the traumatic event in their lives.
Case-control studies nested
within cohort studies can be informative.
However, there are few case-control studies
that are considered when making judgments as to the level
or risk from low-dose radiation exposures.
Now the least informative type
of epidemiologic study is the ecological
or geographic correlation study.
As the saying goes, correlation is not causation
and it is particularly true when you start
with a correlation study.
An example might be correlating lung cancer deaths
with radon levels at the county level -
where you don't know the radon home exposure
for anyone nor do you know how long he
or she lived in the county.
The numbers were large
and negative correlations were reported; however,
the findings were subsequently found to be biased
by smoking status which created these false results.
My favorite correlation story is that back when the causes
of lung cancer were not known,
lung cancer deaths correlated much better with the sale
of refrigerators than the sale of tobacco products.
Ecological studies can be good for generating hypotheses,
but not for testing hypotheses - that is whether
or not some exposures might possibly be causal.
Ecological studies lack quality
and are the most susceptible to bias.
Since there are a lot of these studies in the literature,
it is important to be able to separate the wheat
of the cohort studies from the chaff of the ecological studies.
Of course, no study is perfect.
Radiological epidemiologic studies are primarily
observational: we have to play the hand we're dealt.
Observational studies are not experimental studies.
There are always potential known and unknown biases that we try
to minimize as well as the confounding influences.
These are potential sources of error
that can distort the study findings no matter how much you
try to avoid them.
And there are intrinsic limitations
to study design as just mentioned.
Not all studies are equal
and even the good designs can produce flawed findings!
Another technique used
by epidemiologists is called meta-analysis.
For this type of analysis, you put together all the studies
of a similar topic, such as all the underground uranium miners
and other miners exposed to radon.
You then look for common patterns
and get better precision to estimate radiation risks
and to evaluate possible interaction.
Meta-analyses are generally considered as combining studies
in the literature, whereas pooled analyses are stronger
methodologically because the actual data
from each study is obtained
and a "pooled" analysis is conducted.
There are problems, though, with meta-analyses
and pooled analyses which hamper interpretation, particularly
in radiation epidemiology.
Results for example, can be very dependent
on what studies are included or not included.
Some of the poorest studies can have the largest numbers
and unduly influence the meta-analyses findings.
Some high quality but negative studies may be excluded.
Some studies with negative findings are never published
and this is a form of publication bias.
So, care must be taken in interpreting meta-analyses
of epidemiologic studies of radiation exposed populations.
Not surprisingly, epidemiologic studies are subject
to inaccuracies and uncertainties.
And for epidemiology, there are really three big causes
of these errors: chance, bias and confounding.
Let's discuss each in turn.
Chance. By that, I mean random variation.
You can flip a penny 10 times over and over again,
and one of those times you'll get 10 heads.
In other words, sometimes chance alone produces results far away
from "normal" or what might be expected.
It just happens!
Bias. Bias is a systematic error.
You've done something, or many things,
wrong in the study design or conduct.
Once the study is done, there's nothing you can do
to correct it.
It's biased.
For example, you used ultrasound imaging to screen
for thyroid abnormalities -
but you only screened the exposed group
and not the non-exposed group.
So, you're going to get more thyroid abnormalities
in the exposed people simply because the method
of ascertaining or determining outcome was different.
A recent example comes from the study of atomic bomb survivors.
In the early years, autopsies were performed only
on the high dose survivors who died,
and not on the low dose survivors who died.
Until recently the autopsy results
from high dose survivors were the only ones included
in the cancer incidence evaluations
and biased the findings to be more positive
than they should have been.
Confounding.
Confounders are factors that you hope you can control
in the design or analysis, but often can't
because they are unknown.
We control age at exposure, sex, calendar year,
and try to control for socioeconomic status
as a surrogate for smoking and other health
and behavioral factors.
But any differences by exposure level, in say smoking histories,
can distort your results.
Epidemiology tries its best to get a handle on confounding
but sometimes it's just not possible
because the factors are unknown or unreliably reported.
So, you get erroneous results which lead
to wrong interpretations.
Then there are undue focuses on subgroup analyses
and paper "fuzziness."
Cherry picking, if you will, and lack of clarity or transparency.
When you're reading a paper and the interpretations
and conclusions just don't seem right,
it's probably because they aren't.
Keep your antennae up and be skeptical.
Authors can intentionally or unintentionally focus only
on effects seen in a subgroup, for example in men but not women
or on outcomes that were entirely unexpected
and weren't a prior hypotheses
and hadn't been observed in other studies.
The byline becomes, despite the absence of an overall effect,
we cannot discount the possibility that "fill
in the blank" cancer is a possible low dose effect.
Or what occasionally happens,
is that negative findings are explained as positive
after data manipulations are made.
Be wary if the focus of the paper seems to be
in making headlines and not scientific balance
or understanding.
What makes for a high quality study?
A high-quality study is one
where the authors have minimized the potential for inaccuracies
and accounted for uncertainties.
The United Nations has just published a document
on judging the quality of radiation studies
which might be a good read.
But in general a high quality or reliable study is one that:
Carefully selects participants.
Obtains high-quality information
on radiation exposure measurements.
Traces or follows subjects for long periods
with few being lost to follow-up.
Makes valid and accurate assessments
of individual radiation exposures that lead
to accurate estimates
of radiation dose to organs or tissues.
Validates outcomes of interest, such as cancer.
Collects data on confounding factors to the extent possible
so as to minimize the likelihood of distorting errors.
Performs optimum statistical analyses.
This surprisingly can be a concern when different types
of analyses or different adjustment factors give
meaningfully different results.
Has a protocol that specifies the questions
and study rationale in advance so that tangents are minimized.
The authors don't highlight an unexpected or peculiar result
that wasn't one of the questions trying to be answered.
Has a study size as large as feasible
to minimize the play of chance.
Serious deficiencies in even a few
of these points can move a reliable study
into the unreliable or flawed category!
Be skeptical but then make your own assessment based
on the information before you.