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

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

Author

Thomas Scheike, Klaus K. Holst

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))
#> Strata 'MZ'
#> Strata 'DZ'

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)