tvTran() R function from [dynsurv]

Fit Time-varying Transformation Model for Right Censored Survival Data

Unlike the time-varying coefficient Cox model, the transformation model fomulates the temporal covariate effects in terms of survival function, i.e., [REMOVE_ME] $S(t|X) = g(\beta_0(t)' X), [REMOVE_ME_2]$ 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.


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.

Returns

An object of S3 class tvTran representing the fit.

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.

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.

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 <- 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))
## End(Not run)

References

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

tvTran function