On 2015-08-18 01:44, David Winsemius wrote:
On Aug 17, 2015, at 1:51 PM, David Winsemius wrote:
On Aug 17, 2015, at 12:10 PM, survivalUser wrote:
Dear All,
I would like to build a model, based on survival analysis on some
data, that is able to predict the /*expected time until death*/
for a new data instance.
Are you sure you want to use life expectancy as the outcome? In
order to establish a mathematical expectation you need to have
know the risk at all time in the future, which as pointed out in
the print.survfit help page is undefined unless the last
observation is a death. Very few datasets support such an estimate.
If on the other hand you have sufficient events in the future, then
you may be able to more readily justify an estimate of a median
survival.
Dear survivalUser;
I've been reminded that you later asked for a parametric model built
with survreg. The above commentary applies to the coxph models and
objects and not to survreg objects. If you do have a parametric
model, even with incomplete observation then calculating life
expectancy should be a simple matter of plugging the parameters for
the distribution's mean value, since life-expectancy is the
statistical mean. So maybe you do want such a modle. The default
survreg distribution is "weibull" so just go to your mathematical
statistics text and look up the formula for the mean of a Weibull
distribution with the estimated parameters.
No need for 'the mathematical statistics text': The necessary
information is found on the help page for the Weibull distribution: E(T)
= b Gamma(1 + 1/a), where 'b' is scale (really!) and 'a' is shape. You
must however take into account the special parametrization that is used
by 'survreg'; see its help page for how to do it.
Alternatively, use 'aftreg' in the package 'eha' and get the same
parametrization as in base R.
After getting the baseline expectation by the formula above, simply
multiply that value by exp(-lp) to get the expected life for an
individual with linear predictor lp.
A useful alternative is simulation (use 'rweibull') or numerical
integration, especially for estimating remaining expected life 'later in
life'. And for other distributions than the Weibull.
Göran Broström
(author of the eha package)
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