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relsurv (version 1.6-5)

rsmul: Fit Andersen et al Multiplicative Regression Model for Relative Survival

Description

Fits the Andersen et al multiplicative regression model in relative survival. An extension of the coxph function using relative survival.

Usage

rsmul(formula, data, ratetable = survexp.us, int,na.action,init,
      method,control,...)

Arguments

formula
a formula object, with the response on the left of a ~ operator, and the terms on the right. The terms consist of predictor variables separated by the + operator, along with a ratetable term. The ratetable
data
a data.frame in which to interpret the variables named in the formula.
ratetable
a table of event rates, such as survexp.us.
int
the number of follow-up years used for calculating survival(the data are censored after this time-point). If missing, it is set the the maximum observed follow-up time.
na.action
a missing-data filter function, applied to the model.frame, after any subset argument has been used. Default is options()$na.action.
init
vector of initial values of the iteration. Default initial value is zero for all variables.
method
the default method mul assumes hazard to be constant on yearly intervals. Method mul1 uses the ratetable to determine the time points when hazard changes. The mul1 method is therefore more accurate, but at the same
control
a list of parameters for controlling the fitting process. See the documentation for coxph.control for details.
...
Other arguments will be passed to coxph.control.

Value

  • an object of class coxph.

Details

NOTE: All times used in the formula argument must be specified in days. This is true for the follow-up time as well as for any variables needed ratetable object, like age and year. On the contrary, the int argument requires interval specification in years.

References

Method: Andersen, P.K., Borch-Johnsen, K., Deckert, T., Green, A., Hougaard, P., Keiding, N. and Kreiner, S. (1985) "A Cox regression model for relative mortality and its application to diabetes mellitus survival data.", Biometrics, 41: 921--932. Package. Pohar M., Stare J. (2006) "Relative survival analysis in R." Computer Methods and Programs in Biomedicine, 81: 272--278 Relative survival: Pohar, M., Stare, J. (2007) "Making relative survival analysis relatively easy." Computers in biology and medicine, 37: 1741--1749.

See Also

rsadd, rstrans.

Examples

Run this code
data(slopop)
data(rdata)
#fit a multiplicative model
#note that the variable year is given in days since 01.01.1960 and that 
#age must be multiplied by 365 in order to be expressed in days.
fit <- rsmul(Surv(time,cens)~sex+as.factor(agegr)+ratetable(age=age*365,
      sex=sex,year=year),ratetable=slopop,data=rdata)


#check the goodness of fit
rs.br(fit)

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