rsmul {relsurv} | R Documentation |
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 NOTE: The follow-up time must be in days. |
data |
a data.frame in which to interpret the variables named in
the |
ratetable |
a table of event rates, such as |
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
|
init |
vector of initial values of the iteration. Default initial value is zero for all variables. |
method |
the default method |
control |
a list of parameters for controlling the fitting process.
See the documentation for |
rmap |
an optional list to be used if the variables are not
organized and named in the same way as in the |
... |
Other arguments will be passed to |
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
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)