dynsurv (version 0.4-7)

tvTran: Fit Time-varying Transformation Model for Right Censored Survival Data

Description

Unlike the time-varying coefficient Cox model, the transformation model fomulates the temporal covariate effects in terms of survival function, i.e., $$S(t|X)=g({\beta }_0(t{)}^{\prime }X),$$ where $g(z)=exp(-exp(z))$. It can be viewed as a functional generalized linear model with response $I(T>t)$, and other transformation function is possible. The time-varying coefficients are solved a set of estimating equations sequentially.

Usage

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

Value

An object of S3 class tvTran representing the fit.

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.

Details

Note that because the time-varying coefficient function is connected to the survival function, it has a different interpretation of the time-varying coefficient function in Cox model.

The control argument is a list of components:

resample

A logical value, default TRUE. If TRUE, the model will estimate a 95% confidence band by resampling method.

R

Number of resamplings, default 30.

References

Peng, L. and Huang, Y. (2007). Survival analysis with temporal covariate effects. Biometrika 94(3), 719--733.

See Also

coef.tvTran, plotCoef.

Examples

if (FALSE) {
## 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 <- tvTran(myformula, mydata, control = list(resample = TRUE, R = 30))

## Plot the time-varying coefficient function between two time points
plotCoef(subset(coef(fit), Time > 15 & Time < 175))
}