Selection of controls in a case-control study is difficult enough, but you
also have to worry about the selection of the cases. Do you select incident
cases (for example all breast cancer patients newly diagnosed during a given
time frame) or prevalent cases (for example, all breast cancer patients who
are alive during a given time frame).
These can lead to very different answers, because the probability of
finding a case in a given time frame is related to mortality risk. Those
patients who have a mild form of disease and survive for a relatively long
time have a good chance of being around on the date that you go looking for
them. Those patients who die quickly are unlikely to be around on the date
that you go looking for them.
Let's consider an example with simulated data.
The lines on this graph represent the duration of disease with the left
endpoint representing the date that the disease was first diagnosed and the
right endpoint representing the date that the patient died. The line
segments are ordered from the time of initial diagnosis with patients
diagnosed in 1999 and 2000 at the bottom of the graph and patients diagnosed
in 2003 and 2004 at the top of the graph.
This graph represents a selection of prevalent cases, and the green lines
represent those patients who were alive on January 1, 2002.
This graph represents incident cases, and the green lines represent those
patients newly diagnosed with the disease between January 1, 2001 and
December 31, 2003.
The prevalent cases include very few patients with short survival time,
compared to the incident cases. This becomes more apparent when you reorder
the patients by survival time.
In this graph, the patients with the shortest survival times appear at the
bottom of the graph and the patients with the longest survival times appear
at the top. Notice how rarely the patients with short survival times appear
among the prevalent cases.
This graph shows the incident cases with the patients again sorted by
survival time. Notice that the incident cases include a fair number of
patients with short survival times.
This can make a critical difference for a case control design where you
have risk factors that are associated not with the disease itself, but with
mortality. Any risk factor that makes a person die quickly is going to be
underrepresented among prevalent cases and could lead to a spurious finding.
This is sometimes called Neyman's bias. A good description of this appears in
a Victor Schoenbach article "Sources of Error" on the web:
Prevalence-incidence (Neyman) bias
This is Sackett's term for, among other things, selective survival.
Also included are the phenomena of reversion to normal of signs of previous
clinical events (e.g., "silent" MI's may leave no clear
electrocardiographic evidence some time later) and/or risk factor change
after a pathophysiologic process has been initiated (e.g., a Type A may
change his behavior after an MI), so that studies based on prevalence will
produce a distorted picture of what has happened in terms of incidence.
--
www.epidemiolog.net/evolving/SourcesofError.pdf
The Phillips article referenced below indicates the particular problems
that Neyman's bias can cause when assessing the prognosis of breast cancer
patients. The Sackett article is the basis for some of Schoenbach's comments
above.
