relsurv (version 1.6-5)

rstrans: Fit Cox Proportional Hazards Model in Transformed Time

Description

The function transforms each person's time to his/her probability of dying at that time according to the ratetable. It then fits the Cox proportional hazards model with the transformed times as a response. It can also be used for calculatin the transformed times (no covariates are needed in the formula for that purpose).

Usage

rstrans(formula, data, ratetable, int,na.action,init,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 rest is censored). 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.

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. See coxph.object and coxph.detail for details.
• y
  an object of class Surv containing the transformed times (these times do not depend on covariates).

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: Stare J., Henderson R., Pohar M. (2005) "An individual measure for relative survival." Journal of the Royal Statistical Society: Series C, 54 115--126. 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

rsmul, invtime, rsadd, survexp.

Examples

data(slopop)
data(rdata)

#fit a Cox model using the transformed times
#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 <- rstrans(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)