rsmul: Fit Andersen et al Multiplicative Regression Model for...

In relsurv: Relative Survival

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 = relsurv::slopop, int,na.action,init,
      method,control,rmap,...)

Arguments

formula	a formula object, with the response as a Surv object on the left of a ~ operator, and, if desired, terms separated by the + operator on the right. NOTE: The follow-up time must be in days.
data	a data.frame in which to interpret the variables named in the formula.
ratetable	a table of event rates, such as slopop.
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 time can be more computationally intensive.
control	a list of parameters for controlling the fitting process. See the documentation for coxph.control for details.
rmap	an optional list to be used if the variables are not organized and named in the same way as in the ratetable object. See details below.
...	Other arguments will be passed to coxph.control.

Details

NOTE: The follow-up time must be specified in days. The ratetable being used may have different variable names and formats than the user's data set, this is dealt with by the rmap argument. For example, if age is in years in the data set but in days in the ratetable object, age=age*365.241 should be used. The calendar year can be in any date format (date, Date and POSIXt are allowed), the date formats in the ratetable and in the data may differ.

Value

an object of class coxph with an additional item:

basehaz

Cumulative baseline hazard (population values are seen as offset) at centered values of covariates.

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

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 365.241 in order to be expressed in days.
fit <- rsmul(Surv(time,cens)~sex+as.factor(agegr),rmap=list(age=age*365.241),
            ratetable=slopop,data=rdata)


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

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