PROPHET StatGuide: Examining survival test results to detect assumption violations
The individual observations can be examined for signs of
lack of independence
or lack of uniformity
in the censoring.
When examining survival test results, you should keep these
potential problems in mind, along with the possibility
of
implicit factors
not surfaced in the data (unless
you used stratification
to control for such a factor).
If the results include a Kaplan-Meier plot,
you should also consider those results. You can also examine the Kaplan-Meier plot
for signs of crossing survival functions. If the results include a
life table analysis, you should also
examine those results.
Examining results for a survival test:
- Lack of independence of censoring:
- You should be alert to the possibility of
systematic patterns in the censoring,
For example, if there are many values
censored earlier in the experiment rather
than later, there may have been a change
of conditions during the experiment.
(For example, one physician may have withdrawn
referred patients early on while other
doctors did not.) If there were a
relatively large number of censored
values in a short span of time,
then the censorings may be related.
(For example, a physician transfers to
another hospital, and all referred
patients suddenly leave the study.)
A common problem with a survival analysis
experiment studying medical treatments
is that patients who do not do well
one or more of the treatments must be
withdrawn from the study, so that
sicker patients may be more likely to
have censored survival times.
- Many censored values:
- If there are many censored values, the effective
sample size becomes smaller and the survival
test results become less reliable.
If many subjects
are censored at approximately the same time,
the possibility of a common cause should be
considered.
This would violate the assumption
of independence of censoring
and survival times.
If many subjects are left alive at the end of the study, the study
may simply not have continued long enough to
provide a reliable comparison of survival functions.
- Small sample sizes:
- Small sample sizes tend to violate the
asymptotic estimation assumptions that the Gehan-Breslow,
Mantel-Cox, and Tarone-Ware survival tests rely on.
High censoring rates
also reduce the effective sample size for subsequent
intervals.
- Patterns in plots of data:
- If the assumptions for the censoring and survival distributions
are correct, then a plot of either the censored or the
noncensored values (or both together) against time
should show no particular patterns.
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Last modified: February 20, 1997
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