If you think Statistics is a complex, difficult to understand subject,
you're right, but this page will help remove a lot of the mystery. If
you think Statistics can be twisted and manipulated to produce just
about any desired result, you're right again. But once you know how
the numbers are twisted, it is usually easy to spot the dishonesty.
Note: Epidemiology and Statistics are two different things, but the
words are often used interchangeably. Epidemiology is the study of
illnesses, and usually uses statistics, but there other methods available.
Since almost all studies on health and medicine use epidemiology to
reach their conclusions, understanding how it works is the only way
to sort out the facts from the deceptions and frauds. Once you learn
to pick apart these studies, you'll be able to approach the media with
a very different attitude. When some talking head on TV tells you that
some study proves that coffee is bad for you, and a week later another
head tells you it's good for you, you'll know how to find out which
one is reporting the facts. In many cases, you'll find both of them
Types of Studies
Fact: Cohort Studies follow a
group of people with different exposures to a substance over a period
of time. Tracking people before any health effects occur reduces the
impact of bias and increases the accuracy of the study, and allows testing
for a variety of illnesses. It is the most expensive, time consuming
and difficult type of study to conduct.
Cohort Studies are useful for common illnesses, but are too expensive
and impractical for studying rare diseases.
Fact: Case Control studies examine
two groups of people, those who already have an illness, and a control
group. The control group may contain a random sampling of the population,
or a sample specifically selected because they don't have the illness
Case Control studies are more likely to be biased because they start
by selecting people who are already sick. For instance, if you wanted
to find out if coffee caused stomach cancer, a case control study would
start out with a sample of people who already had stomach cancer, leaving
out the coffee drinkers who remained healthy. Case Control studies are
much less expensive and time consuming, requiring much smaller sample
sizes and eliminating the need to track people over long periods of
time. They are often the only practical way to study uncommon illnesses.
Fact: Meta Studies (more accurately
referred to as Meta Analysis) are analyses of existing studies. The
researcher gathers data from other studies, picks the appropriate ones,
pools the results and extracts his data.
It is extremely difficult to do this with any degree of accuracy,
and extremely easy to twist the results to a predetermined outcome.
Simply leaving out one or two studies can skew the data dramatically
in one direction or the other. Be highly suspicious of any meta
analysis. Carefully check for any researcher bias. If you automatically
reject any meta study conducted or financed by someone with a strong
agenda, you will almost always be right.
There are other types of studies, but these are the most common.
Fact: The goal of an epidemiological
study is to determine Relative Risk (RR).
Relative risk is determined by first establishing a baseline, an accounting
of how common a disease (or condition) is in the general population.
This general rate is given a Relative Risk of 1.0, no risk at all. An
increase in risk would result in a number larger than 1.0. A decrease
in risk would result in a lower number, and indicates a protective effect.
For instance, if a researcher wants to find out how coffee drinking
effects foot fungus, he first has to find out how common foot fungus
is in the general population. In this fictional example, let's say he
determines that 20 out every 1,000 people have foot fungus. That's the
baseline, a RR of 1.0. If he discovers that 30 out of 1,000 coffee drinkers
have foot fungus, he's discovered a fifty percent increase, which would
be expressed as a RR of 1.50.
If he were to find the rate was 40 out of 1,000, it would give him
a RR of 2.0.
He might find foot fungus was less common among coffee drinkers. A
rate of 15 out of 1,000 would be expressed as a RR of 0.75, indicating
that drinking coffee has a protective effect against foot fungus.
The media usually reports RRs as percentages. An RR of 1.40 is usually
reported as a 40% increase, while an RR of .90 is reported as a 10%
decrease. (In theory, at least. In practice, negative RRs are seldom
Note: Some studies calculate an Odds Ratio (OR) instead of an RR. The
formulas for determining the two numbers are different, but when studying
rare diseases the results are approximately the same. When studying
more common diseases ORs tend to overstate the RR.
Fact: As a rule of thumb, an
RR of at least 2.0 is necessary to indicate a cause and effect relationship,
and a RR of 3.0 is preferred.
"As a general rule of thumb, we are looking for a
relative risk of 3 or more before accepting a paper for publication."
- Marcia Angell, editor of the New England Journal of Medicine"
"My basic rule is if the relative risk isn't at least
3 or 4, forget it." - Robert Temple, director of drug evaluation
at the Food and Drug Administration.
"Relative risks of less than 2 are considered small
and are usually difficult to interpret. Such increases may be due to
chance, statistical bias, or the effect of confounding factors that
are sometimes not evident." - The National Cancer Institute
"An association is generally considered weak if
the odds ratio [relative risk] is under 3.0 and particularly when it
is under 2.0, as is the case in the relationship of ETS and lung cancer."
- Dr. Kabat, IAQC epidemiologist
This requirement is ignored in almost all studies of ETS.
While it's important to know the RR, it's also very important to find
the actual numbers. When dealing with the mass media, beware of the
phrase "times more likely."
For instance, a news story may announce "Banana eaters are four times
more likely to get athletes foot!" You find the study, read the abstract
and find the RR is, indeed, 4.0. But further digging may reveal that
the risk went from 1.5 in 10,000 to 6 in 10,000. Technically, the risk
is four times greater, but would you worry about a jump from
0.015% to to 0.06%?
Fact: The Confidence interval
(CI) is used to determine the precision of the RR. It is expressed as
a range of values that would be considered valid, for instance .90 –
The narrower the CI, the more accurate the study. The CI can be narrowed
in many ways, including using more accurate data and a larger sample
Fact: Confidence intervals are
usually calculated to a 95% confidence level. This means the odds of
the results occurring by chance are 5% or less.
This is one reason epidemiology is considered a crude science. (Imagine
if your brakes failed 5% of the time.) The EPA, in their infamous 1993
SHS study, used a 90% CI, doubling their margin of error to achieve
their desired results.
The RR could be any number within the CI. For instance, an RR of 1.15
with a CI of .95 – 1.43 could just as well be a finding of 1.25, an
25% increase, or .96, a 4% decrease, or 1.0, no correlation at all.
Pay close attention to any study where the CI includes 1.0. When the CI includes 1.0, the RR is
not statistically significant. When the lower bound of the CI is near 1.0 the RR is barely statistically significant.
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On average, women live longer than men. Any study on longevity has
to account for this fact. This is called a confounder, which is easy
to remember because it can confound the results of a study. Some studies
use the term "confounding variable." Any study of longevity
(usually referred to as a study of morbidity) which doesn't take this
confounder into account will be very inaccurate. For instance, when
studying the longevity of smokers, it's important to adjust for the
gender difference, and adjust for the percentage of men and women in
Sound complicated? It gets worse. Poor people die sooner than rich
people. Black people die sooner than white people, even when adjusting
for the income confounder. People in some countries live longer than
people in others. So if an impoverished black male smoker in Uruguay
dies before reaching the median age, is it because of his income, race,
gender, smoking, or nationality?
Fact: When studying the effects
of tobacco exposure, either to the smoker or to those around him, confounders
include age, allergies, nationality, race, medications, compliance with
medications, education, gas heating and cooking, gender, socioeconomic
status, exposure to other chemicals, occupation, use of alcohol, use
of marijuana, consumption of saturated fat and other dietary considerations,
family history of cancer and domestic radon exposure, to name a few.
Fact: When studying the effects
of SHS on children confounders include most of the above, plus breast
feeding, crowding, day care and school attendance, maternal age, maternal
symptoms of depression, parental allergies, parental respiratory symptoms
A study that does not account for all of these factors is likely
to be very inaccurate, and is probably worthless.