splineCox() R function from [dynsurv]

Fit Spline Based Cox Model for Right Censored Survival Data

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.


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.

Returns

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.

Note

This function is essentially a wrapper function of coxph for the expanded data set. It does not implements the algorithm disscussed in the reference paper. These authors implemented their algorithm into a tvcox package, which is more efficient for larger data set, but may not be stable compared to coxph.

Examples


## Not run:

## Attach 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)

References

Perperoglou, A., le Cessie, S., & van Houwelingen, H. C. (2006). A fast routine for fitting Cox models with time varying effects of the covariates. Computer Methods and Programs in Biomedicine, 81(2), 154--161.

splineCox function