pe.sasieni: Fits Proportional excess hazards model with fixed offsets

In timereg: Flexible Regression Models for Survival Data

Fits Proportional excess hazards model with fixed offsets

Description

Fits proportional excess hazards model. The Sasieni proportional excess risk model.

Usage

pe.sasieni(
  formula = formula(data),
  data = parent.frame(),
  id = NULL,
  start.time = 0,
  max.time = NULL,
  offsets = 0,
  Nit = 50,
  detail = 0,
  n.sim = 500
)

Arguments

formula	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 models are written using the survival modelling given in the survival package.

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).

Sasieni, P.D., Proportional excess hazards, Biometrika (1996), 127–41.

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

timereg documentation built on Sept. 11, 2024, 8:35 p.m.