Cumulative hazard transformation
Description
Transforms survival times using the cumulative hazard function.
Usage
cumhaz (y, d)
Argument
| y | vector of nonnegative survival times |
| d | vector of censoring indicators, should be the same length as y. If d is missing the data is assumed to be uncensored. |
Value
A vector of transformed survival times.
Note
The primary use of doing a cumulative hazard transformation is that after such a transformation, exponential survival models yield results that are often very much comparable to proportional hazards models. In our implementation of Logic Regression, however, exponential survival models run much faster than proportional hazards models when there are no continuous separate covariates.
Author(s)
Ingo Ruczinski and Charles Kooperberg
References
Ruczinski I, Kooperberg C, LeBlanc ML (2003). Logic Regression, Journal of Computational and Graphical Statistics, 12, 475-511.
See Also
Examples
data(logreg.testdat)
#
# this is not survival data, but it shows the functionality
yy <- cumhaz(exp(logreg.testdat[,1]), logreg.testdat[, 2])
# then we would use
# logreg(resp=yy, cens=logreg.testdat[,2], type=5, ...
# insted of
# logreg(resp=logreg.testdat[,1], cens=logreg.testdat[,2], type=4, ...
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