tvTran {dynsurv} R Documentation

## 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.

### 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.

### Value

An object of S3 class tvTran representing the fit.

### References

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

coef.tvTran, plotCoef.

### 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)


[Package dynsurv version 0.4-5 Index]