Compute a Relative Survival Curve from an additive relative survival model
Computes the predicted relative survival function for an additive relative survival model fitted with maximum likelihood.
rs.surv.rsadd(formula, newdata)
formula |
a |
newdata |
a data frame with the same variable names as those that appear in the |
Does not work with factor variables - you have to form dummy variables before calling the rsadd function.
a survfit object; see the help on survfit.object for details.
The survfit methods are used for print,
plot, lines, and points.
Package. Pohar M., Stare J. (2006) "Relative survival analysis in R." Computer Methods and Programs in Biomedicine, 81: 272–278
survfit,
survexp
data(slopop)
data(rdata)
#fit a relative survival model
fit <- rsadd(Surv(time,cens)~sex+age+year,rmap=list(age=age*365.241),
ratetable=slopop,data=rdata,int=c(0:10,15))
#calculate the predicted curve for a male individual, aged 65, diagnosed in 1982
d <- rs.surv.rsadd(fit,newdata=data.frame(sex=1,age=65,year=as.date("1Jul1982")))
#plot the curve (will result in a step function since the baseline is assumed piecewise constant)
plot(d,xscale=365.241)
#calculate the predicted survival curves for each individual in the data set
d <- rs.surv.rsadd(fit,newdata=rdata)
#calculate the average over all predicted survival curves
p.surv <- apply(d$surv,1,mean)
#plot the relative survival curve
plot(d$time/365.241,p.surv,type="b",ylim=c(0,1),xlab="Time",ylab="Relative survival")Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.