dynsurv (version 0.2-2)

splineCox: Fit Spline Based Cox Model for Right Censored Survival Data

Description

Rearrange the rignt censored survival data in a counting process style. Model the time-varying coefficient function using B-splines. The fit is done by introducing pseudo time-dependent covariates and then calling function coxph in survival package.

Usage

splineCox(formula, data, control=list())

Arguments

formula

a formula object, with the response on the left of a '~' operator, and the terms on the right. The response must be a survival object as returned by the Surv function.

data

a data.frame in which to interpret the variables named in the formula.

control

list of control options.

Value

An object of S3 class splineCox representing the fit.

Details

The control argument is a list of components:

df:

degree of freedom for the B-splines, default 5.

knots:

interior knots point, default NULL. If NULL, the knots will be automatically choosen.

boundary:

lower and upper boundaries for the spline function, default NULL. If NULL, the minimun and maximun finite event time or censoring time will be specified.

References

A. Perperoglou, S. le Cessie, and H.C. van Houwelingen (2006). A fast routine for fitting Cox models with time varying effects of the covariates. Computer Methods and Programs in Biomedicine 81, 154--161.

See Also

coef.splineCox, plotCoef.

Examples

## Not run: 
# # Load the veteran data from the survival package
# mydata <- survival::veteran
# mydata$celltype <- relevel(mydata$celltype, ref="large")
# myformula <- Surv(time, status) ~ karno + celltype
# 
# # Fit the time-varying transformation model
# fit <- splineCox(myformula, mydata, control=list(df=5))
# 
# # Plot the time-varying coefficient function between two time points
# plotCoef(subset(coef(fit), Time > 15 & Time < 175), smooth=TRUE)
# ## End(Not run)