mets (version 1.3.11)

phreg_weibull: Weibull-Cox regression

Description

Fits a Cox-Weibull with cumulative hazard given by $$ \Lambda(t) = \lambda \cdot t^s $$ where \(s\) is the shape parameter, and \(\lambda\) the rate parameter. We here allow a regression model for both parameters $$\lambda := \exp(\beta^\top X)$$ $$s := \exp(\gamma^\top Z)$$ as defined by `formula` and `shape.formula` respectively.

Usage

phreg_weibull(
  formula,
  shape.formula = ~1,
  data,
  save.data = TRUE,
  control = list()
)

Value

`phreg.par` object

Arguments

formula

Formula for proportional hazards. The right-handside must be an [Event] or [Surv] object (with right-censoring and possibly delayed entry).

shape.formula

Formula for shape parameter

data

data.frame

save.data

if TRUE the data.frame is stored in the model object (for predictions and simulations)

control

control arguments to optimization routine [stats::nlmbin]

Author

Klaus Kähler Holst, Thomas Scheike

Details

The parametrization

See Also

[mets::phreg()]

Examples

data(sTRACE, package="mets")
sTRACE$entry <- 0
fit1 <- phreg_weibull(Event(entry, time, status == 9) ~ age,
             shape.formula = ~age, data = sTRACE)
tt <- seq(0,10, length.out=100)
pr1 <- predict(fit1, newdata = sTRACE[1, ], times = tt)
fit2 <- phreg(Event(time, status == 9) ~ age, data = sTRACE)
pr2 <- predict(fit2, newdata = sTRACE[1, ], se = FALSE)
if (interactive()) {
   plot(pr2$times, pr2$surv, type="s")
   lines(tt, pr1[,1,1], col="red", lwd=2)
}