Usage
bicomprisk(formula, data, cause = c(1, 1), cens = 0,
causes, indiv, strata = NULL, id, num,
max.clust = 1000, marg = NULL, se.clusters = 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
Hist object (see example below) and the
left-hand-side specying the covariate structure
cause
Causes (default (1,1)) for which to estimate
the bivariate cumulative incidence
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.
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.
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 lower level functions