splineCox | R Documentation |
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())
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 |
data |
A data.frame in which to interpret the variables named in the
|
control |
List of control options. |
The control
argument is a list of components:
degree of freedom for the B-splines, default 5;
interior knots point, default NULL
. If
NULL
, the knots will be automatically choosen;
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.
An object of S3 class splineCox
representing the fit.
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
.
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.
coef.splineCox
, plotCoef
.
## 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)
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