Description Usage Arguments Author(s) Examples
Estimation of concordance in bivariate competing risks data
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formula |
Formula with left-hand-side being a |
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 |
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 |
Thomas Scheike, Klaus K. Holst
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