Semi-parametric Timevarying Regression
Survival Regression Software
Updated 2008. See
update list for changes from last version, and the
to do list for what's planned.
This page contains an
package for the
fitting various survival models. These all involves aspects of
time-varying effects, and the underlying theory is described
in Martinussen and Scheike (to Appear).
The package contains software for Aalen additive risk model, the
semi-parametric additive risk model by McKeague and Sasieni, the
Cox-Aalen model by Scheike and Zhang, the proportional excess hazards
model by Martinussen and Scheike, and the Cox model with
partly time-varying effects by Martinussen, Scheike and Skovgaard.
The software estimate the parameters of all these models and
do various tests to see if time-varying parameters are significantly
time-varying or significant.
Also two-stage estimation Clayton-Oakes-Glidden
model, and extensions that makes a regression structure
possible for the variance parameters.
In addition to the time-varying models the semiparametric
proportional odds model is also fitted, and a stratified version
of the proportional odds model.
The package also contains goodness-of-fit procedures based on
There is now an offset option in one version of the aalen function,
that can be used for excess hazards modelling in the context of
relative survival. One aspect is that one can test the
proportionality of the Saisieni proportional excess hazards model by
cumulative martingale residuals.
There is a predict function that makes confidence bands for the additive
risk model and the Cox-Aalen survival model (that includes the Cox-model).
In version 1.0-5 you can also do flexible
regression modelling for modelling for competing risks data based on
the IPCW direct binomial regression approach (Scheike, Zhang, Gerds, 2008).
The predict function will make uniform confidence bands for predictions
for the flexible models that includes the Fine-Gray model, and the
non-parametric product limit estimator that is the same as our estimator
in the non-regression case.
Partial least squares (PLS) and Lasso for the additive risk model.
PLS with a predict function.
The code is provided under the
GNU General Public License (2)
The theory is described in detail in the book
by Martinussen and Scheike.
The PDF manual pages and
PS manual pages are here.
The package tarred and gzipped for linux/unix. Un-tar it with something
like tar -zxvf timereg_1.0-1.tar.gz timereg and install with
R CMD INSTALL timereg
, or wihtout super-user privileges
R CMD INSTALL timereg --library localdir and then inside R
.libPaths("localdir"); library(timereg); .
Or install directly with something like:
R CMD INSTALL timereg_1.0-2.tar.gz --library localdir
R 2.6.x timereg 1.1-0.zip
is a Windows port. Download it and select "Install
package from local zip file" from the R "Packages" menu to install.
For MacOS install the linux versions.
See CRAN for versions
ported to other systems (e.g MacOS).
Please let me know if you have any problems installing or using