ClaytonOakes: Clayton-Oakes model with piece-wise constant hazards

In mets: Analysis of Multivariate Event Times

Description

Clayton-Oakes frailty model

Usage

ClaytonOakes(formula, data = parent.frame(), cluster, var.formula = ~1,
  cuts = NULL, type = "piecewise", start, control = list(),
  var.invlink = exp, ...)

Arguments

formula	formula specifying the marginal proportional (piecewise constant) hazard structure with the right-hand-side being a survival object (Surv) specifying the entry time (optional), the follow-up time, and event/censoring status at follow-up. The clustering can be specified using the special function cluster (see example below).
data	Data frame
cluster	Variable defining the clustering (if not given in the formula)
var.formula	Formula specifying the variance component structure (if not given via the cluster special function in the formula) using a linear model with log-link.
cuts	Cut points defining the piecewise constant hazard
type	when equal to two.stage, the Clayton-Oakes-Glidden estimator will be calculated via the timereg package
start	Optional starting values
control	Control parameters to the optimization routine
var.invlink	Inverse link function for variance structure model
...	Additional arguments

Author(s)

Klaus K. Holst

Examples

set.seed(1)
d <- subset(simClaytonOakes(500,4,2,1,stoptime=2,left=2),!truncated)
e <- ClaytonOakes(Surv(lefttime,time,status)~x1+cluster(~1,cluster),
                  cuts=c(0,0.5,1,2),data=d)
e


d2 <- simClaytonOakes(500,4,2,1,stoptime=2,left=0)
d2$z <- rep(1,nrow(d2)); d2$z[d2$cluster%in%sample(d2$cluster,100)] <- 0
## Marginal=Cox Proportional Hazards model:
ts <- ClaytonOakes(Surv(time,status)~prop(x1)+cluster(~1,cluster),
                   data=d2,type="two.stage")
## Marginal=Aalens additive model:
ts2 <- ClaytonOakes(Surv(time,status)~x1+cluster(~1,cluster),
                    data=d2,type="two.stage")
## Marginal=Piecewise constant:
e2 <- ClaytonOakes(Surv(time,status)~x1+cluster(~-1+factor(z),cluster),
                   cuts=c(0,0.5,1,2),data=d2)
e2
plot(ts)
plot(e2,add=TRUE)

e3 <- ClaytonOakes(Surv(time,status)~x1+cluster(~1,cluster),cuts=c(0,0.5,1,2),
                   data=d,var.invlink=identity)
e3

mets documentation built on May 2, 2019, 4:43 p.m.