dynsurv (version 0.2-2)

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., $$S(t|X) = g(\beta_0(t)' X),$$ 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())

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 tvTran representing the fit.

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: [object Object],[object Object]

References

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

See Also

coef.tvTran, plotCoef.

Examples

# 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 <- 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))