R: Predictions for Survival and Competings Risks Regression for...
predict.timereg
R Documentation
Predictions for Survival and Competings Risks Regression for timereg
Description
Make predictions based on the survival models
(Aalen and Cox-Aalen) and the competing risks
models for the cumulative incidence function
(comp.risk). Computes confidence intervals and
confidence bands based on resampling.
Usage
## S3 method for class 'timereg'
predict(object,newdata=NULL,X=NULL,times=NULL,Z=NULL,
n.sim=500,uniform=TRUE,se=TRUE,alpha=0.05,resample.iid=0,...)
Arguments
object
an object belonging to one of the following
classes: comprisk, aalen or cox.aalen
newdata
specifies the data at which the
predictions are wanted.
X
alternative to newdata, specifies the
nonparametric components for predictions.
Z
alternative to newdata, specifies the
parametric components of the model for predictions.
times
times in which predictions are computed, default is all time-points for baseline
n.sim
number of simulations in resampling.
uniform
computes resampling based uniform
confidence bands.
se
computes pointwise standard errors
alpha
specificies the significance levelwhich cause we consider.
resample.iid
set to 1 to return iid decomposition of estimates, 3-dim matrix (predictions x times x subjects)
...
unused arguments - for S3 compatability
Value
time
vector of time points where the predictions are computed.
unif.band
resampling based constant to construct 95% uniform
confidence bands.
model
specifies what model that was fitted.
alpha
specifies the significance level for the confidence intervals.
This relates directly to the constant given in unif.band.
newdata
specifies the newdata given in the call.
RR
gives relative risk terms for Cox-type models.
call
gives call for predict funtion.
initial.call
gives call for underlying object used for predictions.
P1
gives cumulative inicidence predictions for competing risks models.
Predictions given in
matrix form with different subjects in different rows.
S0
gives survival predictions for survival models.
Predictions given in
matrix form with different subjects in different rows.
se.P1
pointwise standard errors for predictions of P1.
se.S0
pointwise standard errors for predictions of S0.
Author(s)
Thomas Scheike, Jeremy Silver
References
Scheike, Zhang and Gerds (2008), Predicting cumulative
incidence probability by direct binomial regression,
Biometrika, 95, 205-220.
Scheike and Zhang (2007), Flexible competing risks regression modelling and goodness of fit, LIDA, 14, 464-483 .
Martinussen and Scheike (2006), Dynamic regression
models for survival data, Springer.