# ClaytonOakes: Clayton-Oakes model with piece-wise constant hazards In mets: Analysis of Multivariate Event Times

 ClaytonOakes R Documentation

## Clayton-Oakes model with piece-wise constant hazards

### 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

Klaus K. Holst

### Examples

```set.seed(1)
d <- subset(simClaytonOakes(500,4,2,1,stoptime=2,left=2),truncated)
e <- ClaytonOakes(survival::Surv(lefttime,time,status)~x+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(survival::Surv(time,status)~timereg::prop(x)+cluster(~1,cluster),
data=d2,type="two.stage")
## Marginal=Aalens additive model:
ts2 <- ClaytonOakes(survival::Surv(time,status)~x+cluster(~1,cluster),
data=d2,type="two.stage")
## Marginal=Piecewise constant:
e2 <- ClaytonOakes(survival::Surv(time,status)~x+cluster(~-1+factor(z),cluster),
cuts=c(0,0.5,1,2),data=d2)
e2

e0 <- ClaytonOakes(survival::Surv(time,status)~cluster(~-1+factor(z),cluster),
cuts=c(0,0.5,1,2),data=d2)
ts0 <- ClaytonOakes(survival::Surv(time,status)~cluster(~1,cluster),
data=d2,type="two.stage")
plot(ts0)
plot(e0,add=TRUE)

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

mets documentation built on Jan. 17, 2023, 5:12 p.m.