00:00
So one of the most
important aspects of information bias is misclassification
bias, that's when the actual records might
be wrong. Many epidemiological investigations
use medical records, we'll look back to see how
diagnoses were made, we'll look at government
registries to see the prevalence of a variety
of diseases, so sometimes those records are
simply wrong. So misclassification is a type
of information bias, it's when some people
have the disease or maybe they're labelled
as not having the disease or vice versa. As
an example, let's say we're trying to compute
the prevalence of menopause suffers in some
population, but sometimes the records say
that someone has menopause based upon their
age alone and sometimes it's based upon whether
or not their menses has ceased. So clearly
two different definitions of the same condition
is being used and that gives us an artificial
problem with a change in definition, it's
a misclassification problem. A more famous
example is when they developed a more accurate
diagnosis for AIDS, a more accurate diagnosis
has to do testing for the HIV virus, but before
that was available to us, we used a visual
definition of AIDS, something called the Bangui
definition, so a checklist of symptoms was
used to determine whether or not a patient
likely was suffering from AIDS. So when the
HIV test came into play, all the previous
definitions of AIDS weren't applicable anymore,
if you look past through the historical prevalence
data, apparently around the mid-1980s, the
prevalence rate shifts dramatically, it wasn't
because something dramatically happened that
was different in terms of the disease itself,
just that the classification of the disease
had changed, that's an information bias, a
misclassification bias.
01:48
When we talk about misclassification bias, we
can either be differential or non-differential,
one of them has to do with a directionality and
the other has to do with a lack of directionality,
let's work through some examples and see if
you can tell what I'm talking about. So for
example, let's say we have a study that is
attempting to measure whether mothers of malformed
babies had more infections during pregnancy
than did mothers of normal babies. And to conduct
this research, we will find some mothers who
have malformed babies and some will have normal
babies and we'll ask them about their experiences
when they first gave birth in the hospital,
whether or not they were more infections around
that time. Women with malformed babies tend
to have more problematic pregnancies in general
and therefore more doctor content, therefore
they're more likely to remember infections
more so than those without malformed babies,
so they are more likely to have a better recognition
or recollection of infection and that gives
us an artificial sense of the relationship
between infection rates when they are giving
birth and the likelihood of having malformed
babies. So in differential misclassification
bias, errors tend to be in one direction.
Let's look at this example. We are using a
blood pressure cuff to take measurements of
both adults and children. Let's say we're
measuring association between blood pressure
and intelligence, or any kind of outcome you
care about, it doesn't really matter, it's
the measurement we care about here. Now as
you probably know, children have smaller arms
than adults, so one blood pressure cuff will
behave differently from both of those populations,
amongst the children it's going to underestimate
blood pressure, amongst adults it probably
will measure it appropriately. So this will
give us a sense that the children have lower
blood pressures than the adults do, that's
differential bias, one group is having a measurement
that is dramatically different from the other
group in one direction only. Now for non-differential
misclassification bias, the readings can be
random, or at the very least, not in one direction
or another. So let's say we have the same
blood pressure example and instead of having
a blood pressure cuff for children and adults,
we're doing one for two groups of adults,
but the cuff doesn't work, it's broken, so
we're going to have random data for both groups,
it doesn't matter which group is having what
direction, we're going to be random and in
fact that's going to bias our results towards
the null hypothesis, we're less likely to
find an affect artificially. So the thing
about misclassification bias is that it's
inherent in the data collection methodology
and probably avoidable.
04:29
A really popular kind of information bias
is a recall bias, we encounter recall bias a
lot in surveys and recall bias is when subjects
have selective memory of events, they recall
things differently.
So it's when the response to a study
questions influenced by the respondents memory
as well as by his actual opinion and our memories
are notoriously unreliable. Here is an example,
in 1995 there is a very famous criminal court
trial, maybe you remember it, it was the O.J.
Simpson murder trial, it was in all the news,
everybody watched it. So let's say you're
doing a study in 2005 and you're doing a random
survey asking people if they thought O.J. was
actually guilty, because he was declared not
guilty in the trial and whether or not they
thought the trial was fair, it's entirely
possible that people who thought he was truly
innocent were more likely to remember the
details because they are more angered by the
result than those who think he's guilty, so
their recall is being affected by their opinions
about the outcome, so that's recall bias.
05:36
Similar to recall bias is interviewer bias.
Interviewer bias is when the researcher elicits
the responses that he or she wants when doing
the interviewing. For example, you can use
your body language, the tone of your voice
or even sort of leaning forward and making
a face like this when someone is saying something
interesting, people are susceptible to triggers
from other individuals, this is why it's important
to have well-trained interviewers who know
to be objective and to not give away their
desires when interviewing people when doing
this sort of research.