a formula object, with the response on the left of a ‘~’ operator, and
the terms on the right. The response must be a survival object as
returned by the ‘Surv’ function.
data
a data.frame with the variables.
id
gives the number of individuals.
start.time
starting time for considered time-period.
max.time
stopping considered time-period if different from 0.
Estimates thus computed from [0,max.time] if max.time>0. Default is max of data.
offsets
fixed offsets giving the mortality.
Nit
number of itterations.
detail
if detail is one, prints iteration details.
n.sim
number of simulations, 0 for no simulations.
Details
The program assumes that there are no ties, and if such are present
random noise is added to break the ties.
Value
Returns an object of type "pe.sasieni".
With the following arguments:
cum
baseline of Cox model excess risk.
var.cum
pointwise variance estimates for estimated cumulatives.
gamma
estimate of relative risk terms of model.
var.gamma
variance estimates for gamma.
Ut
score process for Cox part of model.
D2linv
The inverse of the second derivative.
score
final score
test.Prop
re-sampled absolute supremum values.
pval.Prop
p-value based on resampling.
Author(s)
Thomas Scheike
References
Martinussen and Scheike, Dynamic Regression Models for Survival Data,
Springer Verlag (2006).
Cortese, G. and Scheike, T.H., Dynamic regression hazards models
for relative survival (2007), submitted.
Examples
data(mela.pop)
out<-pe.sasieni(Surv(start,stop,status==1)~age+sex,mela.pop,
id=1:205,Nit=10,max.time=7,offsets=mela.pop$rate,detail=0,n.sim=100)
summary(out)
ul<-out$cum[,2]+1.96*out$var.cum[,2]^.5
ll<-out$cum[,2]-1.96*out$var.cum[,2]^.5
plot(out$cum,type="s",ylim=range(ul,ll))
lines(out$cum[,1],ul,type="s"); lines(out$cum[,1],ll,type="s")
# see also prop.excess function