rs.br: Test the Proportional Hazards Assumption for Relative...

In relsurv: Relative Survival

Test the Proportional Hazards Assumption for Relative Survival Regression Models

Description

Test the proportional hazards assumption for relative survival models (rsadd, rsmul or rstrans) by forming a Brownian Bridge.

Usage

rs.br(fit, sc, rho = 0, test = "max", global = TRUE)

Arguments

fit	the result of fitting a relative survival model, using the rsadd, rsmul or rstrans function.
sc	partial residuals calculated by the resid function. This is used to save time if several tests are to be calculated on these residuals and can otherwise be omitted.
rho	a number controlling the weigths of residuals. The weights are the number of individuals at risk at each event time to the power rho. The default is rho=0, which sets all weigths to 1.
test	a character string specifying the test to be performed on Brownian bridge. Possible values are "max" (default), which tests the maximum absolute value of the bridge, and cvm, which calculates the Cramer Von Mises statistic.
global	should a global Brownian bridge test be performed, in addition to the per-variable tests

Value

an object of class rs.br. This function would usually be followed by both a print and a plot of the result. The plot gives a Brownian bridge for each of the variables. The horizontal lines are the 95 confidence intervals for the maximum absolute value of the Brownian bridge

References

Goodness of fit: Stare J.,Pohar Perme M., Henderson R. (2005) "Goodness of fit of relative survival models." Statistics in Medicine, 24: 3911–3925.

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, rsmul, rstrans, resid.

Examples

data(slopop)
data(rdata)
fit <- rsadd(Surv(time,cens)~sex,rmap=list(age=age*365.241),
		ratetable=slopop,data=rdata,int=5)
rsbr <- rs.br(fit)
rsbr
plot(rsbr)

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