# rs.surv.rsadd: Compute a Relative Survival Curve from an additive relative... In relsurv: Relative Survival

 rs.surv.rsadd R Documentation

## Compute a Relative Survival Curve from an additive relative survival model

### Description

Computes the predicted relative survival function for an additive relative survival model fitted with maximum likelihood.

### Usage

```rs.surv.rsadd(formula, newdata)
```

### Arguments

 `formula` a `rsadd` object (Implemented only for models fitted with the codemax.lik (default) option.) `newdata` a data frame with the same variable names as those that appear in the `rsadd` formula. a predicted curve for each individual in this data frame shall be calculated

### Details

Does not work with factor variables - you have to form dummy variables before calling the rsadd function.

### Value

a `survfit` object; see the help on `survfit.object` for details. The `survfit` methods are used for `print`, `plot`, `lines`, and `points`.

### References

Package. Pohar M., Stare J. (2006) "Relative survival analysis in R." Computer Methods and Programs in Biomedicine, 81: 272–278

### See Also

`survfit`, `survexp`

### Examples

```
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")

```

relsurv documentation built on Dec. 28, 2022, 2:25 a.m.