bicomprisk: Estimation of concordance in bivariate competing risks data

In mets: Analysis of Multivariate Event Times

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 timereg::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 timereg::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 <- timereg::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 May 29, 2024, 3:51 a.m.