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We've talked a little bit about lost to follow-up
bias already when we talked about the Roma
and their antibiotic resistance study. Lost
to follow-up bias happens when some people
are more likely than others to leave a study
and this is a real problem, it happens in
a lot of studies. In fact in almost every
study someone is going to leave before we
are done and it's important that we have a
sense that those who remain in the study are
not systematically different from those who
leave the study. So in some ways lost to follow-up
bias is a kind of non-response bias, because
those we end up analyzing might be systematically
different from those we didn't analyze, so
remember, those who leave can't be too different
from those who stay and we have to have an
effort to assess why they left. Here is an
example. Let's say you're doing a study of
women in an artificial reproduction facility.
00:50
They are all undergoing fertility treatments
because they're struggling to get pregnant
and you're going to follow them over time
and to see whether or not they managed to
become pregnant as a result of these technological
interventions. Some women leave the study,
you don't know what happened to them, you
don't know if they end up getting pregnant
or not. The question you have to ask is, are
they different from those who stayed in the
study? I'm going to suggest to you that maybe
they are, maybe they left because they were
frustrated with the process, maybe they left
because they didn't have enough money to pay
for this expensive process over time, because
this can be an expensive technology. So what
kinds of women don't have money to stay in
the procedure, very often, they are younger
women, because they are not old enough perhaps
to have the salary required to pay for this
procedure over a long period of time. So now
we have the younger women who are likely to
leave. Younger women are also more likely
to get pregnant, so maybe the population that's
leaving your program is also the population
that's most likely to succeed from the intervention,
so you're missing out on a population that
may enhance your outcome and so you're underestimating
the success rate of your intervention, that's
a real problem. So with lost to follow-up
bias, we always try to track those who left
our study to any extent that we can in order
to get a sense of how they might be different
from the population that remains.