MIGUEL HERNAN: Causal graphs are your friends.
And to show you why, in this segment, I'm
going to describe a scientific controversy from the late '70s.
This is a debate that involved some of the most brilliant
clinical investigators at the time.
And by the end of this session, when we will talk a little bit more
about graphs in the next segments, you will
be able to resolve this controversy by yourself
and you will be convinced that causal graphs help us think better.
The 1970s was a fascinating decade that will be remembered for many things,
but one of the most important things that happened during the early '70s
is that for the first time in human history millions of women
were receiving estrogen after menopause.
And this use of estrogen was encouraged by doctors, by the media,
by popular books.
However, investigators were very concerned, because in 1975 and '76,
there were some studies that found that women receiving estrogens
had a higher risk of being diagnosed with endometrial cancer than women
not receiving estrogens.
And the question is why?
There is one obvious answer--
possible answer -- which is that estrogens cause cancer.
But there were two prominent clinical investigators
from Yale, Alvin Feinstein and Ralph Horwitz,
and they proposed another possibility.
They said that estrogens did not have to cause cancer for us
to find this association between estrogen use and cancer.
They said that what might happen is that estrogens simply
lead to a diagnosis of cancers that were already present,
but would not have been diagnosed in the absence of estrogens.
They knew that between 20 and 60% of cancers are silent and asymptomatic,
we only find them when we look for them.
And estrogen use causes uterine bleeding inside women.
So they reasoned that the bleeding was leading doctors
to diagnose preexisting silent cancers.
And as a result, estrogen users have a higher rate of diagnosis of cancer,
even if estrogens did not cause cancer by themselves.
So what Feinstein and Horwitz said, in their own words is,
"If estrogens provoke the bleeding that leads to uterine exam,
asymptomatic uterine cancer is much more likely to be
found in women receiving estrogens than in those who do not receive it."
And they thought that this could fully explain
the association between estrogen use and endometrial cancer.
They refer to this phenomenon as ascertainment bias.
Therefore, we have two possible explanations
for the association between estrogen use and endometrial cancer.
One explanation is that estrogens simply accelerate
the diagnosis of existing cancers, and two, estrogens actually cause cancer.
Now, these two explanations have very different implications
for clinical practice and for public health.
The question is how can we distinguish between these two explanations?
Which one is responsible for the association between estrogen use
and endometrial cancer?
And this is where the debate was initiated
and there was some discrepancies among investigators
that we're going to discuss now.
On the one hand, the investigators from Yale,
they proposed the following approach.
They said, well, since estrogens make some women bleed
and therefore, more likely to be diagnosed with preexisting cancer, when
we analyze data from these studies, let's
restrict the data analysis to women who bleed,
whether they are taking estrogens or not.
If we do that, then all women in the analysis, because they all have bled,
they all have the same likelihood of being diagnosed with cancer
if they have an existing cancer.
And therefore, if we still find an association between estrogen use
and cancer, it must be that estrogens cause cancer.
But there were other equally prominent investigators who disagreed.
People like Hershel Jick, Ken Rothman, and Alec Walker,
from Boston University and Harvard University,
said that restricting to women who bleed was not the solution.
They argued that we would find an association between estrogen and cancer
diagnosis even among women who bleed, and even
if estrogens don't cause cancer.
As a consequence, if that was true, then the approach
that was proposed by the Yale investigators
would not truly distinguish between the two possible explanations.
We would still have some form of ascertainment bias.
And you may be wondering how is this possible?
What the Boston investigators said, it seems counter-intuitive.
Because if all women selected for the analysis experienced bleeding, then
there should be no association due to the differential ascertainment.
Well, we will return to this question at the end of this lesson.
By that time, you'll be able to resolve this problem by yourself.