# bicomprisk: Estimation of concordance in bivariate competing risks data In mets: Analysis of Multivariate Event Times

 bicomprisk R Documentation

## Estimation of concordance in bivariate competing risks data

### Description

Estimation of concordance in bivariate competing risks data

### Usage

```bicomprisk(
formula,
data,
cause = c(1, 1),
cens = 0,
causes,
indiv,
strata = NULL,
id,
num,
max.clust = 1000,
marg = NULL,
se.clusters = NULL,
wname = NULL,
prodlim = FALSE,
messages = TRUE,
model,
return.data = 0,
uniform = 0,
conservative = 1,
resample.iid = 1,
...
)
```

### Arguments

 `formula` Formula with left-hand-side being a `Event` object (see example below) and the left-hand-side specying the covariate structure `data` Data frame `cause` Causes (default (1,1)) for which to estimate the bivariate cumulative incidence `cens` The censoring code `causes` causes `indiv` indiv `strata` Strata `id` Clustering variable `num` num `max.clust` max number of clusters in comp.risk call for iid decompostion, max.clust=NULL uses all clusters otherwise rougher grouping. `marg` marginal cumulative incidence to make stanard errors for same clusters for subsequent use in casewise.test() `se.clusters` to specify clusters for standard errors. Either a vector of cluster indices or a column name in `data`. Defaults to the `id` variable. `wname` name of additonal weight used for paired competing risks data. `prodlim` prodlim to use prodlim estimator (Aalen-Johansen) rather than IPCW weighted estimator based on comp.risk function.These are equivalent in the case of no covariates. These esimators are the same in the case of stratified fitting. `messages` Control amount of output `model` Type of competing risk model (default is Fine-Gray model "fg", see comp.risk). `return.data` Should data be returned (skipping modeling) `uniform` to compute uniform standard errors for concordance estimates based on resampling. `conservative` for conservative standard errors, recommended for larger data-sets. `resample.iid` to return iid residual processes for further computations such as tests. `...` Additional arguments to comp.risk function

### Author(s)

Thomas Scheike, Klaus K. Holst

### References

Scheike, T. H.; Holst, K. K. & Hjelmborg, J. B. Estimating twin concordance for bivariate competing risks twin data Statistics in Medicine, Wiley Online Library, 2014 , 33 , 1193-204

### Examples

```library("timereg")

## Simulated data example
prt <- simnordic.random(2000,delayed=TRUE,ptrunc=0.7,
cordz=0.5,cormz=2,lam0=0.3)
## Bivariate competing risk, concordance estimates
p11 <- bicomprisk(Event(time,cause)~strata(zyg)+id(id),data=prt,cause=c(1,1))

p11mz <- p11\$model\$"MZ"
p11dz <- p11\$model\$"DZ"
par(mfrow=c(1,2))
## Concordance
plot(p11mz,ylim=c(0,0.1));
plot(p11dz,ylim=c(0,0.1));

## entry time, truncation weighting
### other weighting procedure
prtl <-  prt[!prt\$truncated,]
prt2 <- ipw2(prtl,cluster="id",same.cens=TRUE,
time="time",cause="cause",entrytime="entry",
pairs=TRUE,strata="zyg",obs.only=TRUE)

prt22 <- fast.reshape(prt2,id="id")

prt22\$event <- (prt22\$cause1==1)*(prt22\$cause2==1)*1
prt22\$timel <- pmax(prt22\$time1,prt22\$time2)
ipwc <- comp.risk(Event(timel,event)~-1+factor(zyg1),
data=prt22,cause=1,n.sim=0,model="rcif2",times=50:90,
weights=prt22\$weights1,cens.weights=rep(1,nrow(prt22)))

p11wmz <- ipwc\$cum[,2]
p11wdz <- ipwc\$cum[,3]
lines(ipwc\$cum[,1],p11wmz,col=3)
lines(ipwc\$cum[,1],p11wdz,col=3)

```

mets documentation built on Oct. 2, 2022, 5:06 p.m.