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

## Description

Clayton-Oakes frailty model

## Usage

 ```1 2 3``` ```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

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25``` ```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 31, 2017, 1:52 a.m.